%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Repeated Structure Extraction %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Wang11, author = "Yanzhen Wang and Kai Xu and Jun Li and Hao Zhang and Ariel Shamir and Ligang Liu and Zhiquan Cheng and Yueshan Xiong", title = "Symmetry Hierarchy of Man-Made Objects", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2011", pdf = "http://www.cs.sfu.ca/~haoz/pubs/wang_eg11_symh.pdf", project = "http://gruvi.cs.sfu.ca/projects/symmetry-hierarchy-man-made-objects/", abstract = "We introduce symmetry hierarchy of man-made objects, a high-level structural representation of a 3D model providing a symmetry-induced, hierarchical organization of the model's constituent parts. Given an input mesh, we segment it into primitive parts and build an initial graph which encodes inter-part symmetry and connectivity relations, as well as self-symmetries in individual parts. The symmetry hierarchy is constructed from the initial graph via recursive graph contraction which either groups parts by symmetry or assembles connected sets of parts. The order of graph contraction is dictated by a set of precedence rules designed primarily to respect the law of symmetry in perceptual grouping and the principle of compactness of representation. We show that symmetry hierarchy naturally implies a hierarchical segmentation that is more meaningful than those produced by local geometric considerations. We also develop an application of symmetry hierarchies for structural shape editing.", } @Article{Berner11, author = "A. Berner, M. Wand, N. Mitra, D. Mewes, H.-P. Seidel", title = "Shape Analysis with Subspace Symmetries", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2011", project = "http://www.mpi-inf.mpg.de/~aberner/Shape_Analysis_Subspace_Symmetries_EG11.html", pdf = "http://www.mpi-inf.mpg.de/~aberner/subspace_symmetry_eg11.pdf", video = "http://www.youtube.com/watch?v=m8tggG86cYs", abstract = "We address the problem of partial symmetry detection, i.e., the identification of building blocks a complex shape is composed of. Previous techniques identify parts that relate to each other by simple rigid mappings, similarity transforms, or, more recently, intrinsic isometries. Our approach generalizes the notion of partial symmetries to more general deformations. We introduce subspace symmetries whereby we characterize similarity by requiring the set of symmetric parts to form a low dimensional shape space. We present an algorithm to discover subspace symmetries based on detecting linearly correlated correspondences among graphs of invariant features. The detected subspace symmetries along with the modeled variations are useful for a variety of applications including shape completion, non-local and non-rigid denoising. We evaluate our technique on various data sets. We show that for models with pronounced surface features, subspace symmetries can be found fully automatically. For complicated cases, a small amount of user input is used to resolve ambiguities. Our technique computes dense correspondences that can subsequently be used in various applications, such as model repair and denoising.", } @Article{Li10, author = "M. Li and F. C. Langbein and R. R. Martin", title = "Detecting Design Intent in Approximate CAD Models using Symmetry", journal = "Computer-Aided Design", volume = "42", number = "3", year = "2010", pages = "183-201", pdf = "http://ralph.cs.cf.ac.uk/papers/Geometry/designintent.pdf", abstract = "Finding design intent embodied as high-level geometric relations between a CAD model's sub-parts facilitates various tasks such as model editing and analysis. This is especially important for boundary-representation models arising from e.g. reverse engineering or CAD data transfer. These lack explicit information about design intent, and often, the intended geometric relations are only approximately present. We give a novel solution to this problem based on detecting approximate local incomplete symmetries, in a hierarchical decomposition of the model into simpler, more symmetric sub-parts. Design intent is detected as congruencies, symmetries and symmetric arrangements of the leaf-parts in this decomposition. All elementary 3D symmetry types are considered. They may be present only locally in subsets of the leaf-parts, and may also be incomplete, i.e. not all elements required for a symmetry need be present. Adaptive tolerance intervals are used for matching inter-point distances, enabling efficient, robust and consistent detection of approximate symmetries. Doing so avoids finding many spurious relations, reliably resolves ambiguities between relations, and reduces inconsistencies. Experiments show that detected relations reveal significant design intent.", } @Article{Cheng10b, author = "Ming-Ming Cheng and Fang-Lue Zhang and Niloy J. Mitra and Xiaolei Huang and Shi-Min Hu", title = "RepFinder: Finding Approximately Repeated Scene Elements for Image Editing", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2010", pdf = "http://graphics.stanford.edu/~niloy/research/rep_finder/paper_docs/repFinder_sigg10_small.pdf", talk = "http://graphics.stanford.edu/~niloy/research/rep_finder/paper_docs/repFinder_slides.zip", project = "http://graphics.stanford.edu/~niloy/research/scan_consolidation/scan_consolidation_sig_10.html", video = "http://www.youtube.com/v/8rzlnnbS5PI&hl", abstract = "Repeated elements are ubiquitous and abundant in both manmade and natural scenes. Editing such images while preserving the repetitions and their relations is nontrivial due to overlap, missing parts, deformation between instances, illumination variation, etc. Manually enforcing such relations is laborious and error prone. We propose a novel framework where simple user input in the form of scribbles are used to guide detection and extraction of such repeated elements. Our detection process is based on a novel boundary band method, and robustly extracts the repetitions along with their mutual depth relations. We then use topological sorting to establish a partial depth ordering of overlapping repeated instances. Missing parts on occluded instances are completed using information from other instances. The extracted repeated instances can then be seamlessly edited and manipulated for a variety of high level tasks that are otherwise difficult to perform. We demonstrate the versatility of our framework on a large set of inputs of varying complexity, showing applications to image rearrangement, edit transfer, deformation propagation, and instance replacement.", } @Article{Mitra10a, author = "Niloy J. Mitra and Alex Bronstein and Michael Bronstein", title = "Intrinsic Regularity Detection in {3D} Geometry", booktitle = "{ECCV}", year = "2010"", pdf = "http://graphics.stanford.edu/~niloy/research/intrinsic_regularity/paper_docs/intrinsic_regularity_small.pdf", project = "http://graphics.stanford.edu/~niloy/research/intrinsic_regularity/intrinsic_regularity.html", video = "http://www.youtube.com/v/B8dJ5zDhkes", abstract = "Automatic detection of symmetries, regularity, and repetitive structures in 3D geometry is a fundamental problem in shape analysis and pattern recognition with applications in computer vision and graphics. Especially challenging is to detect intrinsic regularity, where the repetitions are on an intrinsic grid, without any apparent Euclidean pattern to describe the shape, but rising out of (near) isometric deformation of the underlying surface. In this paper, we employ multidimensional scaling to reduce the problem of intrinsic structure detection to a simpler problem of 2D grid detection. Potential 2D grids are then identified using an autocorrelation analysis, refined using local fitting, validated, and finally projected back to the spatial domain. We test the detection algorithm on a variety of scanned plaster models in presence of imperfections like missing data, noise and outliers. We also present a range of applications including scan completion, shape editing, super-resolution, and structural correspondence.", } @InProceedings{Mizoguchi10, author = "Tomohiro Mizoguchi and Satoshi Kanai", title = "Decomposing Scanned Assembly Meshes Based on Periodicity Recognition and Its Application to Kinematic Simulation Modeling", booktitle = "Advances in Geometric Modeling and Processing ({GMP})", year = "2010", pdf = "http://www.springerlink.com/content/ku47140174vn1088/fulltext.pdf", abstract = "Along with the rapid growth of industrial X-ray CT scanning systems, it is now possible to non-destructively acquire the entire meshes of assemblies consisting of a set of parts. For the advanced inspections of the assemblies, such as estimation of their assembling errors or examinations of their behaviors in the motions, based on their CT scanned meshes, it is necessary to accurately decompose the mesh and to extract a set of partial meshes each of which correspond to a part. Moreover it is required to create models which can be used for the real-product based simulations. In this paper, we focus on CT scanned meshes of gear assemblies as examples and propose beneficial methods for establishing such advance inspections of the assemblies. We first propose a method that accurately decomposes the mesh into partial meshes each of which corresponds to a gear based on periodicity recognitions. The key idea is first to accurately recognize the periodicity of each gear and then to extract the partial meshes as sets of topologically connected mesh elements where periodicities are valid. Our method can robustly and accurately recognize periodicities from noisy scanned meshes. In contrast to previous methods, our method can deal with single-material CT scanned meshes and can estimate the correct boundaries of neighboring parts with no previous knowledge. Moreover it can efficiently extract the partial meshes from large scanned meshes containing about one million triangles in a few minutes. We also propose a method for creating simulation models which can be used for a gear teeth contact evaluation using extracted partial meshes and their periodicities. Such an evaluation of teeth contacts is one of the most important functions in kinematic simulations of gear assemblies for predicting the power transmission efficiency, noise and vibration. We demonstrate the effectiveness of our method on a variety of artificial and CT scanned meshes.", } @Article{Bokeloh09, author = "M. Bokeloh and A. Berner and M. Wand and H.-P. Seidel and A. Schilling", title = "Symmetry Detection Using Feature Lines", journal = "Computer Graphics Forum", volume = "28", number = "2", pages = "697-706", month = "April", year = "2009", project = "http://www.gris.uni-tuebingen.de/people/staff/bokeloh/project_symmetry2.html", pdf = "http://www.gris.uni-tuebingen.de/people/staff/bokeloh/papers/SymmetryDetectionUsingFeatureLines.pdf", video = "http://www.gris.uni-tuebingen.de/people/staff/bokeloh/papers/line-sym.xvid.avi", talk = "http://www.gris.uni-tuebingen.de/people/staff/bokeloh/papers/talkeg09.zip", abstract = "In this paper, we describe a new algorithm for detecting structural redundancy in geometric data sets. Our algorithm computes rigid symmetries, i.e., subsets of a surface model that reoccur several times within the model differing only by translation, rotation or mirroring. Our algorithm is based on matching locally coherent constellations of feature lines on the object surfaces. In comparison to previous work, the new algorithm is able to detect a large number of symmetric parts without restrictions to regular patterns or nested hierarchies. In addition, working on relevant features only leads to a strong reduction in memory and processing costs such that very large data sets can be handled. We apply the algorithm to a number of real world 3D scanner data sets, demonstrating high recognition rates for general patterns of symmetry.", } @Article{Pauly08, author = "Mark Pauly and Niloy J. Mitra and Johannes Wallner and Helmut Pottmann and Leonidas Guibas", title = "Discovering Structural Regularity in {3D} Geometry", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2008", pdf = "http://graphics.stanford.edu/~niloy/research/structure/paper_docs/structure_sig_08.pdf", talk = "http://graphics.stanford.edu/~niloy/research/structure/paper_docs/structure_slides_sig_08.pdf", project = "http://graphics.stanford.edu/~niloy/research/structure/structure_sig_08.html", abstract = "We introduce a computational framework for discovering regular or repeated geometric structures in 3D shapes. We describe and classify possible regular structures and present an effective algorithm for detecting such repeated geometric patterns in point- or meshbased models. Our method assumes no prior knowledge of the geometry or spatial location of the individual elements that define the pattern. Structure discovery is made possible by a careful analysis of pairwise similarity transformations that reveals prominent lattice structures in a suitable model of transformation space. We introduce an optimization method for detecting such uniform grids specifically designed to deal with outliers and missing elements. This yields a robust algorithm that successfully discovers complex regular structures amidst clutter, noise, and missing geometry. The accuracy of the extracted generating transformations is further improved using a novel simultaneous registration method in the spatial domain. We demonstrate the effectiveness of our algorithm on a variety of examples and show applications to compression, model repair, and geometry synthesis.", } @InProceedings{Berner08, author = "A. Berner and M. Bokeloh and M. Wand and A. Schilling and H.-P. Seidel", title = "A graph-based approach to symmetry detection", booktitle = "{IEEE/EG} Symposium on Volume and Point-Based Graphics", year = "2008", project = "http://www.gris.uni-tuebingen.de/people/staff/berner/graphBasedSymmetry/graphBasedSymmetry.html", talk = "http://www.gris.uni-tuebingen.de/people/staff/berner/graphBasedSymmetry/A_graph-based-Approach-to-Symmetry-Detection_slides.pdf", pdf = "http://www.gris.uni-tuebingen.de/fileadmin/user_upload/Paper/Berner-2008-AGraphBased.pdf", abstract = "Symmetry detection aims at discovering redundancy in the form of recurring structures in geometric objects. In this paper, we present a new symmetry detection algorithm for geometry represented as point clouds that is based on analyzing a graph of surface features. We combine a general feature detection scheme with a RANSAC-based randomized subgraph searching algorithm in order to reliably detect reoc-curring patterns of locally unique structures. A subsequent segmentation step based on a simultaneous region growing variant of the ICP algorithm is applied to verify that the actual point cloud data supports the pattern detected in the feature graphs. We apply our algorithm to synthetic and real-world 3D scanner data sets, demonstrating robust symmetry detection results in the presence of scanning artifacts and noise. The modular and flexible nature of the graph-based detection scheme allows for easy generalizations of the algorithm, which we demonstrate by applying the same technique to other data modalities such as images or triangle meshes.", } @Article{Martinet06, author = "A. Martinet and C. Soler, and N. Holzschuch and F. Sillion", title = "Accurate Detection of Symmetries in {3D} Shapes", journal = "{ACM} Trans. Graphics", volume = "25", number = "2", pages = "439-464", year = "2006", project = "http://artis.imag.fr/Publications/2006/MSHS06/", pdf = "http://artis.imag.fr/Publications/2006/MSHS06/SymmetrieDetectionTOG.pdf", abstract = "We propose an automatic method for finding symmetries of 3D shapes, i.e. isometric transforms which leave a shape globally unchanged. These symmetries are deterministically found through the use of an intermediate quantity: the generalized even moments. By examining their extrema and spherical harmonic coefficients we recover the parameters of the symmetries of the shape. The computation for large composite models is made efficient by using this information in an incremental algorithm capable of recovering the symmetries of a whole shape using the symmetries of its sub-parts. Applications of this work range from coherent re-meshing of geometry with respect to the symmetries of a shape, to geometric compression, intelligent mesh editing and automatic instantiation.", } @Article{Simari06, author = "P. Simari and E. Kalogerakis and K. Singh", title = "Folding Meshes: Hierarchical mesh segmentation based on planar symmetry", booktitle = "ACM Symposium on Geometry Processing ({SGP})", year = "2006", pdf = "http://www.cs.jhu.edu/~psimari/publications/2006_sgp_simari_kalogerakis_singh.pdf", talk = "http://www.cs.jhu.edu/~psimari/publications/2006_sgp_simari_folding_meshes.ppt", abstract = "Meshes representing real world objects, both artist-created and scanned, contain a high level of redundancy due to (possibly approximate) planar reflection symmetries, either global or localized to different subregions. An algorithm is presented for detecting such symmetries and segmenting the mesh into the symmetric and remaining regions. The method, inspired by techniques in Computer Vision, has foundations in robust statistics and is resilient to structured outliers which are present in the form of the non symmetric regions of the data. Also introduced is an application of the method: the folding tree data structure. The structure encodes the non redundant regions of the original mesh as well as the reflection planes and is created by the recursive application of the detection method. This structure can then be unfolded to recover the original shape. Applications include mesh compression, repair, skeletal extraction of objects of known symmetry as well as mesh processing acceleration by limiting computation to non redundant regions and propagation of results.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Shape Abstraction %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Mehra09, author = "R. Mehra and Q. Zhou and J. Long and A. Sheffer and A. Gooch and N. J. Mitra", title = "Abstraction of Man-Made Shapes", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH} Asia)", year = "2009", pdf = "http://www.cs.ubc.ca/labs/imager/tr/2009/Abstraction/abstraction_files/abstraction_siga_09_small.pdf", project = "http://www.cs.ubc.ca/labs/imager/tr/2009/Abstraction/", video = "http://www.cs.ubc.ca/labs/imager/tr/2009/Abstraction/abstraction_files/abstraction_sigA_09.mov", abstract = "Man-made objects are ubiquitous in the real world and in virtual environments. While such objects can be very detailed, capturing every small feature, they are often identified and characterized by a small set of defining curves. Compact, abstracted shape descriptions based on such curves are often visually more appealing than the original models, which can appear to be visually cluttered. We introduce a novel algorithm for abstracting three-dimensional geometric models using characteristic curves or contours as building blocks for the abstraction. Our method robustly handles models with poor connectivity, including the extreme cases of polygon soups, common in models of man-made objects taken from online repositories. In our algorithm, we use a two-step procedure that first approximates the input model using a manifold, closed envelope surface and then extracts from it a hierarchical abstraction curve network along with suitable normal information. The constructed curve networks form a compact, yet powerful, representation for the input shapes, retaining their key shape characteristics while discarding minor details and irregularities.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Example-Based Procedural Modeling %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Merrell11, title = "Interactive Furniture Layout Using Interior Design Guidelines", author = "Paul Merrell and Eric Schkufza and Zeyang Li and Maneesh Agrawala andVladlen Koltun", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2011", video = "http://www.youtube.com/watch?v=iJLY7ZylajU&feature=player_embedded", abstract = "We present an interactive furniture layout system that assists users by suggesting furniture arrangements that are based on interior design guidelines. Our system incorporates the layout guidelines as terms in a density function and generates layout suggestions by rapidly sampling the density function using a hardware-accelerated Monte Carlo sampler. Our results demonstrate that the suggestion generation functionality measurably increases the quality of furniture arrangements produced by participants with no prior training in interior design.", } @Article{Yu11, author = "Lap-Fai Yu and Sai-Kit Yeung and Chi-Keung Tang and Demetri Terzopoulos and Tony F. Chan and Stanley Osher", title = "Make it Home: Automatic Optimization of Furniture Arrangement", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2011", project = "http://www.math.ucla.edu/~saikit/projects/furniture/index.html", video = "http://www.youtube.com/watch?v=vlDoSv6uDKQ&feature=player_embedded", abstract = "This paper presents a fully-automatic system for generating an optimized indoor scene populated by a variety of furniture objects. Given positive examples of furnished indoor scenes, our system extracts hierarchical and spatial relationships for different types of furniture objects. This step is done once, in advance. The extracted relationships are encoded into priors which are integrated into a cost function that optimizes ergonomic factors, such as visibility and accessibility. To deal with the prohibitively large search space, the cost function is optimized by simulated annealing with a Metropolis Hastings state-search step. We demonstrate that different furniture layouts can be automatically synthesized to decorate an indoor scene. A perceptual study is performed to validate that there is no significant difference in preference on functionality between our synthesized results and those produced by human designers.", } @Article{Bokeloh10, author = "Martin Bokeloh and Michael Wand Hans-Peter Seidel", title = "A Connection between Partial Symmetry and Inverse Procedural Modeling", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2010", pdf = "http://www.mpi-inf.mpg.de/~mbokeloh/ProcModFromSym.pdf", talk = "http://www.mpi-inf.mpg.de/~mbokeloh/talk_siggraph2010.zip", project = "http://www.mpi-inf.mpg.de/~mbokeloh/project_dockingSites.html", video = "http://www.youtube.com/v/dGjw67XCbxA&hl", abstract = "In this paper, we address the problem of inverse procedural modeling: Given a piece of exemplar 3D geometry, we would like to find a set of rules that describe objects that are similar to the exemplar. We consider local similarity, i.e., each local neighborhood of the newly created object must match some local neighborhood of the exemplar. We show that we can find explicit shape modification rules that guarantee strict local similarity by looking at the structure of the partial symmetries of the object. By cutting the object into pieces along curves within symmetric areas, we can build shape operations that maintain local similarity by construction. We systematically collect such editing operations and analyze their dependency to build a shape grammar. We discuss how to extract general rewriting systems, context free hierarchical rules, and grid-based rules. All of this information is derived directly from the model, without user interaction. The extracted rules are then used to implement tools for semi-automatic shape modeling by example, which are demonstrated on a number of different example data sets. Overall, our paper provides a concise theoretical and practical framework for inverse procedural modeling of 3D objects.", } @InProceedings{Vanegas10, author = "Carlos A. Vanegas and Daniel G. Aliaga and Bedrich Benes", title = "Building Reconstruction using Manhattan-World Grammars", booktitle = "{CVPR}", year = "2010", pdf = "http://www2.tech.purdue.edu/cgt/facstaff/bbenes/private/papers/Vanegas10CVPR.pdf", video = "http://www.youtube.com/v/s0mhpKFv36g", } @Article{Stava10, author = "Ondrej Stava and Bedrich Benes and Radomir Mech and Daniel Aliaga and Peter Kristof", title = "Inverse Procedural Modeling by Automatic Generation of L-systems", journal = "Computer Graphics Forum", volume = "29", number = "2", pages = "665-674", year = "2010", pdf = "http://www2.tech.purdue.edu/cgt/facstaff/bbenes/private/papers/Stava10EG.pdf", video = "http://www2.tech.purdue.edu/cgt/facstaff/bbenes/private/papers/Stava10EGvideo.zip", abstract = "We present an important step towards the solution of the problem of inverse procedural modeling by generating parametric context-free L-systems that represent an input 2D model. The L-system rules efficiently code the regular structures and the parameters represent the properties of the structure transformations. The algorithm takes as input a 2D vector image that is composed of atomic elements, such as curves and poly-lines. Similar elements are recognized and assigned terminal symbols of an L-system alphabet. The terminal symbols' position and orientation are pair-wise compared and the transformations are stored as points in multiple 4D transformation spaces. By careful analysis of the clusters in the transformation spaces, we detect sequences of elements and code them as L-system rules. The coded elements are then removed from the clusters, the clusters are updated, and then the analysis attempts to code groups of elements in (hierarchies) the same way. The analysis ends with a single group of elements that is coded as an L-system axiom. We recognize and code branching sequences of linearly translated, scaled, and rotated elements and their hierarchies. The L-system not only represents the input image, but it can also be used for various editing operations. By changing the L-system parameters, the image can be randomized, symmetrized, and groups of elements and regular structures can be edited. By changing the terminal and non-terminal symbols, elements or groups of elements can be replaced.", } @InProceedings{Biggers10, author = "Keith Biggers and John Keyser and Jim Wall", title = "Inference-based Generative Modeling of Complex Cluttered Environments", booktitle = "The Interservice/Industry Training, Simulation & Education Conference ({I/ITSEC})", year = "2010", abstract = "The increased use of simulation to support training, testing and evaluation, and rehearsal of operations has resulted in the need for high-fidelity three-dimensional virtual environments. Such environments are often complex and can contain a large number of diverse objects. Manually producing such an extensive model library is an expensive and tedious task. However, reproducing identical objects repeatedly throughout a scene can decrease the underlying realism of the environment thereby reducing user immersion. As a result, various procedural/generative modeling techniques have been developed for automatically constructing variations of models. These techniques have provided methods for generating buildings/cities, vegetation, and terrain. In this paper, we present a novel generative modeling technique centered on an inference-based construction algorithm for developing diverse models from a set of templates. Our approach takes as input a set of example models provided by the user. The algorithm samples and extracts surface patches from these models, and develops a Petri net structure used by an inference-based algorithm for properly fitting patches in a consistent fashion. Our approach uses this generated structure along with a defined parameterization (either user-defined through a simple sketch interface, or algorithmically defined through various methods) to automatically construct objects of varying sizes and configurations. These variations include arbitrary articulation and repetition of parts sampled from the input models. Our approach is capable of generating a rich collection of different solid model representations. Finally, we show an application of our approach for generating complex cluttered environments and show their use in simulation. This paper presents a complete overview of our developed methodology. We provide a survey of the related work, describe our algorithm in detail, and provide example results of varying data complexity. Finally, we describe the technical challenges we encountered, the solutions developed to address these difficulties, and affirm our motivation by providing future work and final conclusions.", } @Article{Merrell10a, author = "P. Merrell and E. Schkufza and V. Koltun", title = "Computer-Generated Residential Building Layouts", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH Asia})", year = "2010", pdf = "http://graphics.stanford.edu/~pmerrell/floorplan-final.pdf", abstract = "We present a method for automated generation of building layouts for computer graphics applications. Our approach is motivated by the layout design process developed in architecture. Given a set of high-level requirements, an architectural program is synthesized using a Bayesian network trained on real-world data. The architectural program is realized in a set of floor plans, obtained through stochastic optimization. The floor plans are used to construct a complete three-dimensional building with internal structure. We demonstrate a variety of computer-generated buildings produced by the presented approach.", } @Article{Merrell10b, author = "P. Merrell and D. Manocha", title = "Model Synthesis: A General Procedural Modeling Algorithm", journal = "{IEEE} Transactions on Visualization and Computer Graphics", year = "2010", pdf = "http://graphics.stanford.edu/~pmerrell/tvcg.pdf", } @Article{Merrell08, author = "P. Merrell and D. Manocha", title = "Continuous Model Synthesis", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH Asia})", year = "2008", pdf = "http://graphics.stanford.edu/~pmerrell/continuous.pdf", video = "http://graphics.stanford.edu/~pmerrell/continuous.wmv", abstract = "We present a novel method for procedurally modeling large complex shapes. Our approach is general-purpose and takes as input any 3D polyhedral model provided by a user. The algorithm exploits the connectivity between the adjacent boundary features of the input model and computes an output model that has similar connected features and resembles the input. No additional user input is needed to guide the model generation and the algorithm proceeds automatically. In practice, our algorithm is simple to implement and can generate a variety of complex shapes representing buildings, landscapes, and 3D fractal shapes in a few minutes.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Curve Skeletons %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Miklos10, author = "Balint Miklos and Joachim Giesen and Mark Pauly", title = "Discrete Scale Axis Representations for {3D} Geometry", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", volume = "29", number = "3", year = "2010"", pdf = "http://lgg.epfl.ch/publications/2010/discrete_scale_axis_26_04_2010.pdf", } @Article{Qian10, author = "Z. Qian and A. Tagliasacchi and B. Chen and D. Cohen-Or and A. Sharf and A. Sheffer and H. Zhang", title = "Consensus Skeleton for Non-rigid Space-time Registration", journal = "Computer Graphics Forum (Proc EUROGRAPHICS)", year = "2010", pdf = "http://www.cs.sfu.ca/~haoz/pubs/zheng_eg10.pdf", } @Article{Cao10, author = "Junjie Cao and Andrea Tagliasacchi and Matt Olson and Hao Zhang and Zhixun Su", title = "Point Cloud Skeletons via Laplacian-Based Contraction", journal = "Computers and Graphics (Proc {SMI})", year = "2010", pdf = "http://www.cs.sfu.ca/~haoz/pubs/cao_smi10.pdf", 187-197", } @Article{Tagliasacchi09, author = "Andrea Tagliasacchi and Hao Zhang and Daniel Cohen-Or", title = "Curve Skeleton Extraction from Incomplete Point Cloud", journal = "ACM Trans. on Graphics (Proc {SIGGRAPH})", volume = "28", number = "3", year = "2009", pdf = "http://www.cs.sfu.ca/~haoz/pubs/sig09_rosa.pdf", } @Article{Giesen09, author = "Joachim Giesen and Balint Miklos and Mark Pauly and Camille Wormser", title = "The Scale Axis Transform", journal = "Computer Graphics Forum (Proc {SGP})", year = "2009", pdf = "http://data.agg.ethz.ch/publications/2009/scale_axis_transform_year = "2009".pdf", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Non-Rigid Surface Correspondence %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Tevs11, author = "A. Tevs and A. Berner and M. Wand and I. Ihrke and {H.-P.} Seidel", title = "Intrinsic Shape Matching by Planned Landmark Sampling", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2011", project = "http://www.tevs.eu/project_eg11.html", pdf = "http://www.tevs.eu/files/eg11_plansac.pdf", abstract = "Recently, the problem of intrinsic shape matching has received a lot of attention. A number of algorithms have been proposed, among which random-sampling-based techniques have been particularly successful due to their generality and efficiency. We introduce a new sampling-based shape matching algorithm that uses a planning step to find optimized "landmark" points. These points are matched first in order to maximize the information gained and thus minimize the sampling costs. Our approach makes three main contributions: First, the new technique leads to a significant improvement in performance, which we demonstrate on a number of benchmark scenarios. Second, our technique does not require any keypoint detection. This is often a significant limitation for models that do not show sufficient surface features. Third, we examine the actual numerical degrees of freedom of the matching problem for a given piece of geometry. In contrast to previous results, our estimates take into account unprecise geodesics and potentially numerically unfavorable geometry of general topology, giving a more realistic complexity estimate.", } @InProceedings{Chang10, author = "W. Chang, H. Li, N. Mitra, M. Pauly, M. Wand", title = "Geometric Registration for Deformable Shapes", booktitle = "Eurographics 2010 course", year = "2010", project = "http://www.mpi-inf.mpg.de/resources/deformableShapeMatching/", } @Article{Tung10, author = "Tony Tung and Takashi Matsuyama", title = "Dynamic Surface Matching by Geodesic Mapping for {3D} Animation Transfer", booktitle = "{CVPR}", year = "2010", } @Article{Sahillioglu10, author = "Yusuf Sahillioglu and Yucel Yemez", title = "{3D} Shape Correspondence by Isometry-Driven Greedy Optimization", booktitle = "booktitle = "{CVPR}", year = "2010", } @Article{Bronstein10, author = "A. M. Bronstein and M. M. Bronstein and R. Kimmel and M. Mahmoudi and G. Sapiro", title = "A Gromov-Hausdorff framework with diffusion geometry for topologically-robust non-rigid shape matching", journal = "Intl. Journal of Computer Vision (IJCV)", volume = "89", number = "2-3", month = "September", year = "2010", pages = "266-286", pdf = "http://visl.technion.ac.il/bron/publications/BroBroKimMahSapIJCV.pdf", } @Article{Cheng10a, author = "Z.-Q. Cheng and W. Jiang and G. Dang and R. Martin and J. Li and H.-H. Li and Y. Chen and Y.-Z. Wang and B. Li and K. Xu and S.-Y. Jin", title = "Non-rigid Registration in {3D} Implicit Vector Space", journal = "Computers and Graphics (Proc {SMI})", year = "2010", pages = "37-46", pdf = "http://ralph.cs.cf.ac.uk/papers/Geometry/NonrigidRegistration.pdf", } @Article{Au10, author = "Oscar Kin-Chung Au and Chiew-Lan Tai and Daniel Cohen-Or and Youyi Zheng and Hongbo Fu", title = "Electors Voting for Fast Automatic Shape Correspondence", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2010", } @Article{Ovsjanikov10, author = "M. Ovsjanikov and Q. Mérigot and F. Mémoli and L. Guibas", title = "One Point Isometric Matching with the Heat Kernel", journal = "Computer Graphics Forum (Proc. ({SGP})", year = "2010"", pdf = "http://graphics.stanford.edu/projects/lgl/papers/ommg-opimhk-10/ommg-opimhk-10.pdf", } @Article{Chen10, author = "Jiun-Hung Chen and Ke Colin Zheng and Linda Shapiro", title = "{3D} Point Correspondence by Minimum Description Length in Feature Space", booktitle = "{ECCV}", year = "2010", } @Article{vanKaick10, author = "Oliver {van Kaick} and Hao Zhang and Ghassan Hamarneh and Daniel Cohen-Or", title = "A Survey on Shape Correspondence", journal = "Computer Graphics Forum (Eurographics State-of-the-Art Report)", year = "2010", pdf = "http://www.cs.sfu.ca/~haoz/pubs/vanKaick_cgf10_survey.pdf", } @Article{Lipman09, author = "Yaron Lipman and Thomas Funkhouser", title = "Mobius Voting for Surface Correspondence", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2009"", pdf = "http://www.cs.princeton.edu/~funk/mobius.pdf", } @Article{Ovsjanikov09, author = "M. Ovsjanikov and A. M. Bronstein and M. M. Bronstein and L. J. Guibas", title = "ShapeGoogle: a computer vision approach for invariant shape retrieval", booktitle = "Proc. Workshop on Nonrigid Shape Analysis and Deformable Image Alignment (NORDIA)", year = "2009", pdf = "http://graphics.stanford.edu/projects/lgl/papers/obbg-sg-09/obbg-sg-09.pdf", } @Article{Huang08, author = "Qixing Huang and Bart Adams and Martin Wicke and Leonidas J. Guibas", title = "Non-Rigid Registration Under Isometric Deformations", journal = "Computer Graphics Forum (Proc {SGP})", volume = "27", number = "5", year = "2008", pages = "1149-1458", pdf = "http://graphics.stanford.edu/projects/lgl/papers/hawg-nrrid-08/hawg-nrrid-08.pdf", } @Article{Jain07, author = "Varun Jain and Hao Zhang and Oliver van Kaick", title = "Non-Rigid Spectral Correspondence of Triangle Meshes", journal = "International Journal on Shape Modeling", volume = "13", number = "1", year = "2007", pages = "101-124", pdf = "http://www.cs.sfu.ca/~haoz/pubs/jain_zhang_ijsm07.pdf", } @Article{Bronstein06, author = "A. M. Bronstein and M. M. Bronstein and R. Kimmel", title = "Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching", journal = "Proc. National Academy of Sciences (PNAS),", volume = "103", number = "5", year = "2006", pages = "1168-1172", pdf = "http://visl.technion.ac.il/bron/publications/BroBroKimPNAS06.pdf", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Template Fitting %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Shaji10, author = "Appu Shaji and Aydin Varol and Lorenzo Torresani and Pascal Fua", title = "Simultaneous Point Matching and {3D} Deformable Surface Reconstruction", booktitle = "{CVPR}", year = "2010", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Feature Detection %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Sunkel11, author = "M. Sunkel, S. Jansen, M. Wand, E. Eisemann, H.-P. Seidel", title = "Learning Line Features in 3D Geometry", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2011", project = "http://www.mpi-inf.mpg.de/~sjansen/Learning_Line_Features_EG11.html", pdf = "http://www.mpi-inf.mpg.de/~sjansen/pdfs/Learning_Line_Features_eg11.pdf", video = "http://www.mpi-inf.mpg.de/~sjansen/EG-Paper1200-Video-Final.mp4", abstract = "Feature detection in geometric datasets is a fundamental tool for solving shape matching problems such as partial symmetry detection. Traditional techniques usually employ a priori models such as crease lines that are unspecific to the actual application. Our paper examines the idea of learning geometric features. We introduce a formal model for a class of linear feature constellations based on a Markov chain model and propose a novel, efficient algorithm for detecting a large number of features simultaneously. After a short user-guided training stage, in which one or a few example lines are sketched directly onto the input data, our algorithm automatically finds all pieces of geometry similar to the marked areas. In particular, the algorithm is able recognize larger classes of semantically similar but geometrically varying features, which is very difficult using unsupervised techniques. In a number of experiments, we apply our technique to point cloud data from 3D scanners. The algorithm is able to detect features with very low rates of false positives and negatives and to recognize broader classes of similar geometry (such as “windows” in a building scan) even from few training examples, thereby significantly improving over previous unsupervised techniques.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Feature-Based Recognition %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Bronstein11, title = "Shape google: Geometric words and expressions for invariant shape retrieval", author = "Alexander Bronstein and Michael Bronstein and Leonidas Guibas and Maks Ovsjanikov", journal = "{ACM} Trans Graphics", volume = "30", number = "1", year = "2011", pdf = "http://visl.technion.ac.il/bron/publications/OvsBroBroGuiNORDIA09.pdf", project = "http://www.inf.usi.ch/bronstein/research_bofs.html", talk = "http://visl.technion.ac.il/bron/presentations/PresentationSIAM10.pptx" abstract = "The computer vision and pattern recognition communities have recently witnessed a surge of feature-based methods in object recognition and image retrieval applications. These methods allow representing images as collections of visual words and treat them using text search approaches following the bag of features paradigm. In this article, we explore analogous approaches in the 3D world applied to the problem of nonrigid shape retrieval in large databases. Using multiscale diffusion heat kernels as geometric words, we construct compact and informative shape descriptors by means of the bag of features approach. We also show that considering pairs of geometric words ( geometric expressions ) allows creating spatially sensitive bags of features with better discriminative power. Finally, adopting metric learning approaches, we show that shapes can be efficiently represented as binary codes. Our approach achieves state-of-the-art results on the SHREC 2010 large-scale shape retrieval benchmark.", } @Article{Bariya10, author = "Prabin Bariya and Ko Nishino", title = "Scale-Hierarchical {3D} Object Recognition in Cluttered Scenes", booktitle = "{CVPR}", year = "2010", } @Article{Hsiao10, author = "Edward Hsiao and Alvaro Collet and Martial Hebert", title = "On improving point-based {3D} recognition", booktitle = "{CVPR}", year = "2010", } @Article{Drost10, author = "Bertram Drost and Markus Ulrich and Nassir Navab and Slobodan Ilic", title = "Model Globally, Match Locally: Efficient and Robust {3D} Object Recognition", booktitle = "{CVPR}", year = "2010", } @Article{Wu10, author = "Huai-Yu Wu", title = "Global and Local Isometry-Invariant Descriptor for {3D} Shape Comparison and Partial Matching", booktitle = "{CVPR}", year = "2010", } @Article{Knopp10, author = "Jan Knopp and Mukta Prasad and Geert Willems) and Radu Timofte and Luc van Gool", title = "Hough Transform and {3D} {SURF} for Robust Three Dimensional Classification", booktitle = "{CVPR}", year = "2010", } @Article{Lian10, author = "Zhouhui Lian and Afzal Godil and Xianfang Sun", title = "Visual Similarity Based {3D} Shape Retrieval Using Bag-of-Features", journal = "Computers and Graphics {SMI}", year = "2010", } @Article{Dey10, author = "Tamal K. Dey and Kuiyu Li and Chuanjiang Luo and Pawas Ranjan and Issam Safa and Yusu Wang", title = "Persistent Heat Signature for Pose-oblivious Matching of Incomplete Models", journal = "Computer Graphics Forum (Proc {SGP})", year = "2010", } @Article{Jain07, author = "Varun Jain and Hao Zhang", title = "A Spectral Approach to Shape-Based Retrieval of Articulated {3D} Models", journal = "Computer-Aided Design", volume = "39", number = "5", year = "2007", pages = "398-407", pdf = "http://www.cs.sfu.ca/~haoz/pubs/jain_zhang_cad07.pdf", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Reflective/Rotational Symmetry Detection %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Lipman10, author = "Yaron Lipman and Xiaobai Chen and Ingrid Daubechies and Thomas Funkhouser", title = "Symmetry Factored Embedding And Distance ", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2010", } @Article{Ghosh10, author = "Deboshmita Ghosh and Nina Amenta and Michael Kazhdan", title = "Closed-form Blending of Local Symmetries", journal = "Computer Graphics Forum (Proc {SGP})", year = "2010", } @Article{Kim10, author = "Vladimir G. Kim and Yaron Lipman and Xiaobai Chen and Thomas Funkhouser", title = "Mobius Transformations For Global Intrinsic Symmetry Analysis", journal = "Computer Graphics Forum (Proc {SGP})", year = "2010", } @Article{Raviv10, author = "D. Raviv and A. M. Bronstein and M. M. Bronstein and R. Kimmel", title = "Full and partial symmetries of non-rigid shapes", journal = "Intl. Journal of Computer Vision (IJCV)", volume = "89", number = "1", year = "2010", pages = "18-39", pdf = "http://visl.technion.ac.il/bron/publications/RavBroBroKimIJCV.pdf", } @Article{Xu09, author = "Kai Xu and Hao Zhang andrea Tagliasacchi and Ligang Liu and Guo Li and Min Meng and Yueshan Xiong", title = "Partial Intrinsic Reflectional Symmetry of {3D} Shapes", journal = "ACM Trans. on Graphics (Proc {SIGGRAPH} Asia)", year = "2009", pdf = "http://www.cs.sfu.ca/~haoz/pubs/siga09_pirs.pdf", project = "https://sites.google.com/site/kevinkaixu/publications/pirs", video = "http://www.youtube.com/v/eO_ZACTJtL0", abstract = "While many 3D objects exhibit various forms of global symmetries, prominent intrinsic symmetries which exist only on parts of an object are also well recognized. Such partial symmetries are often seen as more natural compared to a global one, especially on a composite shape. We introduce algorithms to extract partial intrinsic reflectional symmetries (PIRS) of a 3D shape. Given a closed 2-manifold mesh, we develop a voting scheme to obtain an intrinsic reflectional symmetry axis (IRSA) transform, which computes a scalar field over the mesh so as to accentuate prominent IRSAs of the shape. We then extract a set of explicit IRSA curves on the shape based on a refined measure of local reflectional symmetry support along a curve. The iterative refinement procedure combines IRSA-induced region growing and region-constrained symmetry support refinement to improve accuracy and address potential issues due to rotational symmetries in the shape. We show how the extracted IRSA curves can be incorporated into a conventional mesh segmentation scheme so that the implied symmetry cues can be utilized to obtain more meaningful results. We also demonstrate the use of IRSA curves for symmetry-driven part repair.", } @Article{Li08a, author = "M. Li and F. C. Langbein and R. R. Martin", title = "Detecting Approximate Symmetries of Discrete Point Subsets", journal = "Computer Aided Design", volume = "40", number = "1", year = "2008", pages = "76-93", pdf = "http://ralph.cs.cf.ac.uk/papers/Geometry/appsym.pdf", } @Article{Ovsjanikov08, author = "Maks Ovsjanikov and Jian Sun and Leonidas Guibas", title = "Global Intrinsic Symmetries of Shapes", journal = "Computer Graphics Forum", volume = "27", number = "5", year = "2008", pages = "1341-1348", pdf = "http://graphics.stanford.edu/projects/lgl/papers/osg-giss-08/osg-giss-08.pdf", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Pose Determination %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Fu08, author = "Hongbo Fu and Daniel Cohen-Or and Gideon Dror and Alla Sheffer", title = "Upright Orientation of Man-Made Objects", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2008", } @Article{Aiger08, author = "Dror Aiger and Niloy J. Mitra and Daniel Cohen-Or", title = "4-points Congruent Sets for Robust Surface Registration", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2008", pdf = "http://graphics.stanford.edu/~niloy/research/fpcs/paper_docs/fpcs_sig_08.pdf", project = "http://graphics.stanford.edu/~niloy/research/fpcs/fpcs_sig_08.html", talk = "http://graphics.stanford.edu/~niloy/research/fpcs/paper_docs/fpcs_slides_sig_08.pdf", code = "http://graphics.stanford.edu/~niloy/research/fpcs/4PCS_demo.html", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Surface Properties %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Lai10b, author = "Rongjie Lai and Yonggang Shi and Kevin Scheibel and Scott Fears and Roger Woods and Arthur Toga and Tony Chan", title = "Metric-Induced Optimal Embedding for Intrinsic {3D} Shape Analysis", booktitle = "{CVPR}", year = "2010", } @Article{Kurtek10, author = "Sebastian Kurtek and Eric Klassen and Anuj Srivastava and Zhaohua Ding", title = "A Novel Riemannian Framework for Shape Analysis of {3D} Objects", booktitle = "{CVPR}", year = "2010", } @Article{Sun10, author = "Jian Sun and Xiaobai Chen and Thomas A. Funkhouser", title = "Fuzzy Geodesics and Consistent Sparse Correspondences For Deformable Shapes", journal = "Computer Graphics Forum (Proc {SGP})", year = "2010"", } @Article{BenChen10, author = "Mirela {Ben-Chen} and Adrian Butscher and Justin Solomon and Leonidas Guibas", title = "On Discrete Killing Vector Fields and Patterns on Surfaces", journal = "Computer Graphics Forum (Proc {SGP})", year = "2010"", } @Article{Shapira09, author = "Lior Shapira and Ariel Shamir", title = "Local Geodesic Parametrization: An Ant's Perspective", journal = "Mathematical Foundations of Scientific Visualization Computer Graphics and Massive Data Exploration Series: Mathematics and Visualization", year = "2009"", } @Article{Shalom09, author = "Shy Shalom and Lior Shapira and Ariel Shamir and Daniel Cohen-Or", title = "A Part-aware Surface Metric for Shape Processing", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2009", pdf = "http://www.cs.sfu.ca/~haoz/pubs/liu_eg09_partaware.pdf", } @Article{CohenOr07, author = "Daniel {Cohen-Or} and Ran Gal and Ariel Shamir", title = "Pose Oblivious Shape Signature", journal = "{IEEE} Transactions of Visualization and Computer Graphics ({TVCG})", volume = "13", number = "2", year = "2007", pages = "261-271", } @Article{Shamir06, author = "Ariel Shamir and Lior Shapira and Daniel Cohen-Or", title = "Mesh Analysis Using Geodesic Mean Shift", journal = "The Visual Computer", volume = "22", number = "2", year = "2006", pages = "99-108", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Content-Aware Mesh Processing %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Zheng11, author = "Youyi Zheng and Hongbo Fu and Daniel Cohen-Or and Oscar Kin-Chung Au and Chiew-Lan Tai", title = "Component-wise Controllers for Structure-Preserving Shape Manipulation", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2011", pdf = "http://visgraph.cse.ust.hk/projects/controller/paper1108_final.pdf", video = "http://visgraph.cse.ust.hk/projects/controller/controller.wmv", project = "http://visgraph.cse.ust.hk/projects/controller/", abstract = "Recent shape editing techniques, especially for man-made models, have gradually shifted focus from maintaining local, low-level geometric features to preserving structural, high-level characteristics like symmetry and parallelism. Such new editing goals typically require a pre-processing shape analysis step to enable subsequent shape editing. Observing that most editing of shapes involves manipulating their constituent components, we introduce component-wise controllers that are adapted to the component characteristics inferred by shape analysis. The controllers capture the natural degrees of freedom of individual components and thus provide an intuitive user interface for editing. A typical model often results in a moderate number of controllers, allowing easy establishment of semantic relations among them by automatic shape analysis supplemented with user interaction. We propose a component-wise propagation algorithm to automatically preserve the established inter-relations while maintaining the defining characteristics of individual controllers and respecting the user-specified modeling constraints. We extend these ideas to a hierarchical setup, allowing the user to adjust the tool complexity with respect to the desired modeling complexity. We demonstrate the effectiveness of our technique on a wide range of engineering models with structural features, often containing multiple connected pieces.", } @Article{Gal09, author = "Ran Gal and Olga Sorkine and Niloy Mitra and Daniel Cohen-Or", title = "{iWIRES:} An Analyze-and-Edit Approach to Shape Manipulation", journal = "{ACM} Trans. Graphics (Proc {SIGGRAPH})", year = "2009", pdf = "http://www.cs.tau.ac.il/~galran/papers/iWires/iWires.pdf", project = "http://www.cs.tau.ac.il/~galran/papers/iWires/", video = "http://www.cs.tau.ac.il/~galran/papers/iWires/iWires.mov", abstract = "Man-made objects are largely dominated by a few typical features that carry special characteristics and engineered meanings. State-of-the-art deformation tools fall short at preserving such characteristic features and global structure. We introduce iWires, a novel approach based on the argument that man-made models can be distilled using a few special 1D wires and their mutual relations. We hypothesize that maintaining the properties of such a small number of wires allows preserving the defining characteristics of the entire object. We introduce an analyze-and-edit approach, where prior to editing, we perform a light-weight analysis of the input shape to extract a descriptive set of wires. Analyzing the individual and mutual properties of the wires, and augmenting them with geometric attributes makes them intelligent and ready to be manipulated. Editing the object by modifying the intelligent wires leads to a powerful editing framework that retains the original design intent and object characteristics. We show numerous results of manipulation of man-made shapes using our editing technique.", } @Article{Cabral09, author = "M. Cabral and S. Lefebvre and C. Dachsbacher and G. Drettakis", title = "Structure-Preserving Reshape for Textured Architectural Scenes", journal = "Computer Graphics Forum", year = "2009", project = "http://www-sop.inria.fr/reves/Basilic/2009/CLDD09/", pdf = "http://www-sop.inria.fr/reves/Basilic/2009/CLDD09/meshpuzzleEG09.pdf", video = "http://www-sop.inria.fr/reves/Basilic/2009/CLDD09/meshpuzzle_final_divx.avi", abstract = "Modeling large architectural environments is a difficult task due to the intricate nature of these models and the complex dependencies between the structures represented. Moreover, textures are an essential part of architectural models. While the number of geometric primitives is usually relatively low (i.e., many walls are flat surfaces), textures actually contain many detailed architectural elements. We present an approach for modeling architectural scenes by reshaping and combining existing textured models, where the manipulation of the geometry and texture are tightly coupled. For geometry, preserving angles such as floor orientation or vertical walls is of key importance. We thus allow the user to interactively modify lengths of edges, while constraining angles. Our texture reshaping solution introduces a measure of directional autosimilarity, to focus stretching in areas of stochastic content and to preserve details in such areas. We show results on several challenging models, and show two applications: Building complex road structures from simple initial pieces and creating complex game-levels from an existing game based on pre-existing model pieces.", } @Article{Golovinskiy09a, author = "Aleksey Golovinskiy and Joshua Podolak and Thomas Funkhouser", title = "Symmetry-Aware Mesh Processing", journal = "Mathematics of Surfaces" volume = "LNCS 5654", year = "2009", pdf = "http://www.cs.princeton.edu/~funk/symmetryaware.pdf", video = "http://www.cs.princeton.edu/gfx/pubs/Golovinskiy_2009_SMP/134_video_divx.avi", abstract = "Perfect, partial, and approximate symmetries are pervasive in 3D surface meshes of real-world objects. However, current digital geometry processing algorithms generally ignore them, instead focusing on local shape features and differential surface properties. This paper investigates how detection of large-scale symmetries can be used to guide processing of 3D meshes. It investigates a framework for mesh processing that includes steps for symmetrization (applying a warp to make a surface more symmetric) and symmetric remeshing (approximating a surface with a mesh having symmetric topology). These steps can be used to enhance the symmetries of a mesh, to decompose a mesh into its symmetric parts and asymmetric residuals, and to establish correspondences between symmetric mesh features. Applications are demonstrated for modeling, beautification, and simplification of nearly symmetric surfaces." } @Article{Kraevoy08, author = "Vladislav Kraevoy and Alla Sheffer and Ariel Shamir and Daniel Cohen-Or", title = "Non-homogeneous Resizing of Complex Models", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH} ASIA)", volume = "27", number = "5", year = "2008", project = "http://www.cs.ubc.ca/~vlady/conres/conres.htm", pdf = "http://www.cs.ubc.ca/~vlady/conres/ConResizeAsia_final.pdf", abstract = "Resizing of 3D models can be very useful when creating new models or placing models inside different scenes. However, uniform scaling is limited in its applicability while straightforward non-uniform scaling can destroy features and lead to serious visual artifacts. Our goal is to define a method that protects model features and structures during resizing. We observe that typically, during scaling some parts of the models are more vulnerable than others, undergoing undesirable deformation. We automatically detect vulnerable regions and carry this information to a protective grid defined around the object, defining a vulnerability map. The 3D model is then resized by a space-deformation technique which scales the grid non-homogeneously while respecting this map. Using space-deformation allows processing of common models of man-made objects that consist of multiple components and contain non-manifold structures. We show that our technique resizes models while suppressing undesirable distortion, creating models that preserve the structure and features of the original ones.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Model Composition %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Chaudhuri11, author = "Siddhartha Chaudhuri and Evangelos Kalogerakis and Leonidas Guibas and Vladlen Koltun", title = "Probabilistic Reasoning for Assembly-Based 3D Modeling", journal = "{ACM} Trans Graphics ({Proc SIGGRAPH})", year = "2011"", project = "http://graphics.stanford.edu/~sidch/projects/assembly/", video = "http://www.youtube.com/watch?feature=player_embedded&v=7Abki79WIOY", abstract = "Providing easy-to-use tools for the creation of detailed three-dimensional content is a key challenge for computer graphics research. Assembly-based modeling is a promising approach to making 3D modeling widely accessible. The advantage of assembly-based modeling is that users need not create new geometry from scratch; modeling reduces to selection and placement of components extracted from a database. A key challenge in assembly-based 3D modeling is the identification of components that are relevant to the modeler's intent. In this paper, we present a probabilistic reasoning approach to assembly-based modeling. Our approach studies a model library to learn how shapes are put together, and uses this knowledge to suggest semantically and stylistically relevant components to the user at each stage of the modeling process. To facilitate rapid creation of detailed 3D models by novices, we have developed a prototype assembly-based modeling tool that allows shapes to be composed via drag-and-drop. Suggested components are shown by category, with more relevant suggestions appearing first. When a component is dragged in, it is snapped and glued to the rest of the model for easy assembly. In our experiments, users with little or no modeling experience became proficient with the tool after only a few minutes of training and constructed detailed, attractive 3D models.", } @Article{Chaudhuri10, author = "Siddhartha Chaudhuri and Vladlen Koltun", title = "Data-Driven Suggestions for Creativity Support in {3D} Modeling", journal = "{ACM} Trans Graphics ({Proc SIGGRAPH Asia})", volume = "29", year = "2010"", pdf = "http://graphics.stanford.edu/~sidch/docs/sigasia2010.pdf", video = "http://graphics.stanford.edu/~sidch/videos/sigasia2010.mov", abstract = "We introduce data-driven suggestions for 3D modeling. Datadriven suggestions support open-ended stages in the 3D modeling process, when the appearance of the desired model is ill-defined and the artist can benefit from customized examples that stimulate creativity. Our approach computes and presents components that cand be added to the artists current shape. We describe shape retrieval and shape correspondence techniques that support the generation of data-driven suggestions, and report preliminary experiments with a tool for creative prototyping of 3D models.", } @Article{Sharf06, author = "Andrei Sharf and Marina Blumenkrants and Ariel Shamir and Daniel Cohen-Or", title = "SnapPaste: An Interactive Technique for Easy Mesh Composition", booktitle = "Pacific Graphics", year = "2006", project = "http://www.cs.bgu.ac.il/~asharf/Projects/SNAP/index_.htm", pdf = "http://www.cs.bgu.ac.il/~asharf/Projects/SNAP/Snap-PG06_122.pdf", video = "http://www.cs.bgu.ac.il/~asharf/Projects/SNAP/Snap-PG06_122.zip", abstract = "Editing and manipulation of existing 3D geometric objects are means to extend their repertoire and promote their availability. Traditionally, tools to compose or manipulate objects defined by 3D meshes are in the realm of artists and experts. In this paper, we introduce a simple and effective user interface for easy composition of 3D mesh-parts for non-professionals. Our technique borrows from the cut-and-paste paradigm where a user can cut parts out of existing objects and paste them onto others to create new designs. To assist the user attach objects to each other in a quick and simple manner, many applications in computer graphics support the notion of snapping. Similarly, our tool allows the user to loosely drag one mesh part onto another with an overlap, and lets the system snap them together in a graceful manner. Snapping is accomplished using our Soft-ICP algorithm which replaces the global transformation in the ICP algorithm with a set of point-wise locally supported transformations. The technique enhances registration with a set of rigid to elastic transformations that account for simultaneous global positioning and local blending of the objects. For completeness of our framework, we present an additional simple mesh-cutting tool, adapting the graph-cut algorithm to meshes.", } @Article{Kraevoy07, author = "V. Kraevoy and D. Julius and A. Sheffer",, title = "Shuffler: Modeling with Interchangeable Parts", journal = "Visual Computer", year = "2007", talk = "http://www.cs.ucy.ac.cy/ayia-napa06/presentations/alla_sheffer.pdf", project = "http://www.cs.ubc.ca/~vlady/shuffler/shuffler.htm", pdf = "http://www.cs.ubc.ca/~vlady/shuffler/Shuffler.pdf", video = "http://www.cs.ubc.ca/~vlady/shuffler/shuffler.mov", abstract = "Following the increasing demand to make the creation and manipulation of 3D geometry simpler and more accessible, we introduce a modeling approach that allows even novice users to create sophisticated models in minutes. Our approach is based on the observation that in many modeling settings users create models which belong to a small set of model classes, such as humans or quadrupeds. The models within each class typically share a common component structure. Following this observation, we introduce a modeling system which utilizes this common component structure allowing users to create new models by shuffling interchangeable components between existing models. To enable shuffling, we develop a method for computing a compatible segmentation of input models into meaningful, interchangeable components. Using this segmentation our system lets users create new models with a few mouse clicks, in a fraction of the time required by previous composition techniques. We demonstrate that the shuffling paradigm allows for easy and fast creation of a rich geometric content.", } @Article{Funkhouser04, author = "Thomas Funkhouser and Michael Kazhdan and Philip Shilane and Patrick Min and William Kiefer and Ayellet Tal and Szymon Rusinkiewicz and David Dobkin", title = "Modeling by Example", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2004", pdf = "http://www.cs.princeton.edu/~funk/sig04a.pdf", video = "http://www.cs.princeton.edu/gfx/proj/shape/movies/mbe_hq.avi", abstract = "In this paper, we investigate a data-driven synthesis approach to constructing 3D geometric surface models. We provide methods with which a user can search a large database of 3D meshes to find parts of interest, cut the desired parts out of the meshes with intelligent scissoring, and composite them together in different ways to form new objects. The main benefit of this approach is that it is both easy to learn and able to produce highly detailed geometric models - the conceptual design for new models comes from the user, while the geometric details come from examples in the database. The focus of the paper is on the main research issues motivated by the proposed approach: (1) interactive segmentation of 3D surfaces, (2) shape-based search to find 3D models with parts matching a query, and (3) composition of parts to form new models. We provide new research contributions on all three topics and incorporate them into a prototype modeling system. Experience with our prototype systemindicates that it allows untrained users to create interesting anddetailed 3D models.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Contextual Recognition %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Fisher11, author = "Matthew Fisher and Manolis Savva and Pat Hanrahan", title = "Characterizing Structural Relationships in Scenes Using Graph Kernels", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2011", pdf = "http://graphics.stanford.edu/~mdfisher/Data/GraphKernel.pdf", abstract = "Modeling virtual environments is a time consuming and expensive task that is becoming increasingly popular for both professional and casual artists. The model density and complexity of the scenes representing these virtual environments is rising rapidly. This trend suggests that data-mining a 3D scene corpus to facilitate collaborative content creation could be a very powerful tool enabling more efficient scene design. In this paper, we show how to represent scenes as graphs that encode models and their semantic relationships. We then define a kernel between these relationship graphs that compares common virtual substructures in two graphs and captures the similarity between their corresponding scenes. We apply this framework to several scene modeling problems, such as finding similar scenes, relevance feedback, and context-based model search. We show that incorporating structural relationships allows our method to provide a more relevant set of results when compared against previous approaches to model context search.", } @Article{Fisher10, author = "Matthew Fisher and Pat Hanrahan", title = "Context-based search for {3D} models", journal = "{ACM} Trans Graphics", volume = "29", number = "6", year = "2010", pdf = "http://graphics.stanford.edu/~mdfisher/Data/Context.pdf", abstract = "Large corpora of 3D models, such as Google 3D Warehouse, are now becoming available on the web. It is possible to search these databases using a keyword search. This makes it possible for designers to easily include existing content into new scenes. In this paper, we describe a method for context-based search of 3D scenes. We first downloaded a large set of scene graphs from Google 3D Warehouse. These scene graphs were segmented into individual objects. We also extracted tags from the names of the models. Given the object shape, tags, and spatial relationship between pairs of objects, we can predict the strength of a relationship between a candidate model and an existing object in the scene. Using this function, we can perform context-based queries. The user specifies a region in the scene they are modeling using a 3D bounding box, and the system returns a list of related objects. We show that context-based queries perform better than keyword queries alone, and that without any keywords our algorithm still returns a relevant set of models.", } @Article{Galleguillos10, author = "Carolina Galleguillos and Serge Belongie", title = "Context based object categorization: A critical survey", journal = "Computer Vision and Image Understanding", year = "2010", pdf = "http://vision.ucsd.edu/~carolina/files/cviu_context_review.pdf", abstract = "The goal of object categorization is to locate and identify instances of an object category within an image. Recognizing an object in an image is difficult when images include occlusion, poor quality, noise or background clutter, and this task becomes even more challenging when many objects are present in the same scene. Several models for object categorization use appearance and context information from objects to improve recognition accuracy. Appearance information, based on visual cues, can successfully identify object classes up to a certain extent. Context information, based on the interaction among objects in the scene or global scene statistics, can help successfully disambiguate appearance inputs in recognition tasks. In this work we address the problem of incorporating different types of contextual information for robust object categorization in computer vision. We review different ways of using contextual information in the field of object categorization, considering the most common levels of extraction of context and the different levels of contextual interactions. We also examine common machine learning models that integrate context information into object recognition frameworks and discuss scalability, optimizations and possible future approaches.", } @InProceedings{Xiong11, author = "X. Xiong and D. Munoz and J.A. Bagnell amd M. Hebert", title = "3-D Scene Analysis via Sequenced Predictions over Points and Regions", booktitle = "International Conference on Robotics and Automation - {ICRA}", year = "2011", pdf = "http://www.ri.cmu.edu/pub_files/2011/5/xiong_icra_11.pdf", abstract = "We address the problem of understanding scenes from 3-D laser scans via per-point assignment of semantic labels. In order to mitigate the difficulties of using a graphical model for modeling the contextual relationships among the 3-D points, we instead propose a multi-stage inference procedure to capture these relationships. More specifically, we train this procedure to use point cloud statistics and learn relational information (e.g., tree-trunks are below vegetation) over fine (point-wise) and coarse (region-wise) scales. We evaluate our approach on three different datasets, that were obtained from different sensors, and demonstrate improved performance.", } @InProceedings{Munoz10, author = "D. Munoz and J.A. Bagnell and M. Hebert" title = "Stacked Hierarchical Labeling" booktitle = "{ECCV}", year = "2010" project = "http://www.cs.cmu.edu/~dmunoz/projects/shl.html", pdf = "http://www.ri.cmu.edu/pub_files/2010/9/munoz_eccv_10.pdf", abstract = "In this work we propose a hierarchical approach for labeling semantic objects and regions in scenes. Our approach is reminiscent of early vision literature in that we use a decomposition of the image in order to encode relational and spatial information. In contrast to much existing work on structured prediction for scene understanding, we bypass a global probabilistic model and instead directly train a hierarchical inference procedure inspired by the message passing mechanics of some approximate inference procedures in graphical models. This approach mitigates both the theoretical and empirical diculties of learning probabilistic models when exact inference is intractable. In particular, we draw from recent work in machine learning and break the complex inference process into a hierarchical series of simple machine learning subproblems. Each subproblem in the hierarchy is designed to capture the image and contextual statistics in the scene. This hierarchy spans coarse-to-fine regions and explicitly models the mixtures of semantic labels that may be present due to imperfect segmentation. To avoid cascading of errors and over-fitting, we train the learning problems in sequence to ensure robustness to likely errors earlier in the inference sequence and leverage the stacking approach developed by Cohen et al.", } @Article{Oh10, author = "Sang Oh and Anthony Hoogs and Matt Turek and Roderic Collins", title = "Content-based Retrieval of Functional Objects in Video using Scene Context", booktitle = "{ECCV}", year = "2010", } @Article{Alvarez10, author = "Jose M. Alvarez and Theo Gevers and Antonio M. Lopez", title = "{3D} Scene Priors for Road Detection", booktitle = "{CVPR}", year = "2010", } @Article{Hedau10, author = "Varsha Hedau and Derek Hoiem and David Forsyth", title = "Thinking Inside the Box: Using Appearance Models and Context Based on Room Geometry", booktitle = "{ECCV}", year = "2010", } @Article{Jain10, author = "Arpit Jain and Abhinav Gupta and Larry Davis", title = "Learning What and How of Contextual Models for Scene Labeling", booktitle = "{ECCV}", year = "2010", } @Article{Avraham10, author = "Tamar Avraham and Michael Lindenbaum", title = "Non-Local Characterization of Scenery Images: Statistics, {3D} Reasoning, and a Generative Model", booktitle = "{ECCV}", year = "2010", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Part Correspondence %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{vanKaick11, author = "Oliver {van Kaick} and Andrea Tagliasacchi and Oana Sidi and Hao Zhang and Daniel {Cohen-Or} and Lior Wolf and Ghassan Hamarneh", title = "Prior Knowledge for Part Correspondence", journal = "Computer Graphics Forum (Proc Eurographics)", year = "2011", pdf = "http://www.cs.sfu.ca/~ovankaic/personal/pubs/corr_knowledge.pdf", } @Article{Xu10, author = "Kai Xu and Honghua Li and Hao Zhang and Daniel Cohen-Or and Yueshan Xiong and Zhiquan Cheng", title = "Style-Content Separation by Anisotropic Part Scales", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH} ASIA)", year = "2010", pdf = "http://www.cs.sfu.ca/~haoz/pubs/xu_siga10_style.pdf", } @Article{Shapira09, author = "Lior Shapira and Shy Shalom and Ariel Shamir and Daniel Cohen-Or", title = "Contextual Part Analogies in {3D} Objects ", journal = "International Journal of Computer Vision", volume = "89", number = "2-3", year = "2009", pdf = "http://www.cs.sfu.ca/~haoz/pubs/ijcv10_analogy.pdf", } @Article{Golovinskiy09b, author = "Aleksey Golovinskiy and Thomas Funkhouser", title = "Consistent Segmentation of {3D} Models", journal = "Computers and Graphics (Proc {SMI})", year = "2009", pdf = "http://www.cs.princeton.edu/~funk/smi09.pdf", } @InProceedings{Kraovoy07, author = "V. Kraevoy and D. Julius and A. Sheffer", title = "Shuffler: Modeling with Interchangeable Parts", booktitle = "Pacific Graphics", year = "2007", pdf = "http://www.cs.ubc.ca/~vlady/shuffler/Shuffler.pdf", project = "http://www.cs.ubc.ca/~vlady/shuffler/shuffler.htm", talk = "http://www.cs.ubc.ca/~vlady/shuffler/shuffler_sl.pdf", video = "http://www.cs.ubc.ca/~vlady/shuffler/shuffler.mov", abstract = "Following the increasing demand to make the creation and manipulation of 3D geometry simpler and more accessible, we introduce a modeling approach that allows even novice users to create sophisticated models in minutes. Our approach is based on the observation that in many modeling settings users create models which belong to a small set of model classes, such as humans or quadrupeds. The models within each class typically share a common component structure. Following this observation, we introduce a modeling system which utilizes this common component structure allowing users to create new models by shuffling interchangeable components between existing models. To enable shuffling, we develop a method for computing a compatible segmentation of input models into meaningful, interchangeable components. Using this segmentation our system lets users create new models with a few mouse clicks, in a fraction of the time required by previous composition techniques. We demonstrate that the shuffling paradigm allows for easy and fast creation of a rich geometric content.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Structure Visualization %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Ovsjanikov11, author = "Maksim Ovsjanikov and Wilmot Li and Leonidas Guibas and Niloy J. Mitra", title = "Exploration of Continuous Variability in Collections of 3D Shapes", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2011", } @Article{Mitra10b, author = "Niloy J. Mitra and Yong-Liang Yang and Dong-Ming Yan and Wilmot Li and Maneesh Agrawala", title = "Illustrating How Mechanical Assemblies Work", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2010", pdf = "http://graphics.stanford.edu/~niloy/research/how_things_work/paper_docs/howThingsWork_sigg10_small.pdf", talk = "http://graphics.stanford.edu/~niloy/research/how_things_work/paper_docs/howThingsWork_slides.zip", project = "http://graphics.stanford.edu/~niloy/research/how_things_work/howThingsWork_sig10.html", video = "http://vis.berkeley.edu/papers/howthingswork/motionvis_sig10.mov", abstract = "How things work visualizations use a variety of visual techniques to depict the operation of complex mechanical assemblies. We present an automated approach for generating such visualizations. Starting with a 3D CAD model of an assembly, we first infer the motions of individual parts and the interactions between parts based on their geometry and a few user specified constraints. We then use this information to generate visualizations that incorporate motion arrows, frame sequences and animation to convey the causal chain of motions and mechanical interactions between parts. We present results for a wide variety of assemblies.", } @InProceedings{Karpenko10, author = "Olga Karpenko, Wilmot Li, Niloy J. Mitra and Maneesh Agrawala", title = "Exploded View Diagrams of Mathematical Objects", booktitle = "{IEEE} Visualization", year = "2010", pages = "1311-1318", project = "http://vis.berkeley.edu/papers/methexpview/", pdf = "http://vis.berkeley.edu/papers/methexpview/math_exploded_view_small.pdf", abstract = "We present a technique for visualizing complicated mathematical surfaces that is inspired by hand-designed topological illustrations. Our approach generates exploded views that expose the internal structure of such a surface by partitioning it into parallel slices, which are separated from each other along a single linear explosion axis. Our contributions include a set of simple, prescriptive design rules for choosing an explosion axis and placing cutting planes, as well as automatic algorithms for applying these rules. First we analyze the input shape to select the explosion axis based on the detected rotational and reflective symmetries of the input model. We then partition the shape into slices that are designed to help viewers better understand how the shape of the surface and its cross-sections vary along the explosion axis. Our algorithms work directly on triangle meshes, and do not depend on any specific parameterization of the surface. We generate exploded views for a variety of mathematical surfaces using our system.", } @Article{Li08b, author = "W. Li and M. Agrawala and B. Curless and D. Salesin", title = "Automated generation of interactive 3D exploded view diagrams", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2008", project = "http://vis.berkeley.edu/papers/exview3D/", pdf = "http://vis.berkeley.edu/papers/exview3D/exview3D-SIG08.pdf", video = "http://vis.berkeley.edu/papers/exview3D/exview3D-SIG08.mov", abstract = "We present a system for creating and viewing interactive exploded views of complex 3D models. In our approach, a 3D input model is organized into an explosion graph that encodes how parts explode with respect to each other. We present an automatic method for computing explosion graphs that takes into account part hierarchies in the input models and handles common classes of interlocking parts. Our system also includes an interface that allows users to interactively explore our exploded views using both direct controls and higher-level interaction modes.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Mesh Segmentation %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Kalogerakis10, author = "Evangelos Kalogerakis and Aaron Hertzmann and Karan Singh", title = "Learning {3D} Mesh Segmentation and Labeling ", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2010", } @Article{Skraba10, author = "P. Skraba and M. Ovsjanikov and F. Chazal and L. Guibas", title = "Persistence-based Segmentation of Deformable Shapes", Workshop on Non-Rigid Shape Analysis and Deformable Image Alignment (NORDIA)", year = "2010", pdf = "http://graphics.stanford.edu/projects/lgl/papers/socg-pbsds-10/socg-pbsds-10.pdf", } @Article{Chen09, author = "Xiaobai Chen and Aleksey Golovinskiy and Thomas Funkhouser", title = "A Benchmark for {3D} Mesh Segmentation", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2009", pdf = "http://www.cs.princeton.edu/~funk/segeval.pdf", } @Article{Simari09, author = "P. Simari and D. Nowrouzezahrai and E. Kalogerakis and K. Singh", title = "Multi-objective shape segmentation and labeling", journal = "Computer Graphics Forum (Proc {SGP})", volume = "28", number = "5", year = "2009"", pdf = "http://www.dgp.toronto.edu/~kalo/papers/more/multiobjectivesegmentation_sgp.pdf", } @Article{Huang09, author = "Qixing Huang and Martin Wicke and Bart Adams and Leonidas Guibas", title = "Shape Decomposition Using Modal Analysis", journal = "Computer Graphics Forum (Proc Eurographics)", volume = "28", number = "2", year = "2009"", pdf = "http://graphics.stanford.edu/projects/lgl/papers/hwag-sduma-09/hwag-sduma-09.pdf", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Primitive Fitting %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Schnabel09, author = "R Schnabel and P Degener and Reinhard Klein" title = "Completion and reconstruction with primitive shapes", journal = "Computer Graphics Forum", volume = "28", number = "2", pages = "503-512", year = "2009", abstract = "We consider the problem of reconstruction from incomplete point-clouds. To find a closed mesh the reconstruction is guided by a set of primitive shapes which has been detected on the input point-cloud (e.g. planes, cylinders etc.). With this guidance we not only continue the surrounding structure into the holes but also synthesize plausible edges and corners from the primitives' intersections. To this end we give a surface energy functional that incorporates the primitive shapes in a guiding vector field. The discretized functional can be minimized with an efficient graph-cut algorithm. A novel greedy optimization strategy is proposed to minimize the functional under the constraint that surface parts corresponding to a given primitive must be connected. From the primitive shapes our method can also reconstruct an idealized model that is suitable for use in a CAD system.", } @Article{Ullrich08, author = "Torsten Ullrich and Volker Settgast and Dieter Fellner", title = "Semantic fitting and reconstruction", journal = "Journal on Computing and Cultural Heritage ({JOCCH})", volume = "1", number = "2", month = "October", year = "2008", } @Article{Simari05, author = "Patricio Simari and Karan Singh", title = "Extraction and remeshing of ellipsoidal representations from mesh data", booktitle = "Graphics Interface", year = "2005", pdf = "http://www.cs.jhu.edu/~psimari/publications/2005_gi_simari_singh.pdf", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Statistical Models of Object Classes %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Hasler09, author = "N. Hasler and C. Stoll and M. Sunkel and B. Rosenhahn and {H.-P.} Seidel", title = "A Statistical Model of Human Pose and Body Shape", journal = "Computer Graphics Forum (Proc Eurographics)", volume = "28", number = "2", pages = "337-346" year = "2009", project = "http://www.mpi-inf.mpg.de/resources/scandb/", pdf = "http://www.mpi-inf.mpg.de/~hasler/download/HasStoSunRosSei09Human.pdf", code = "http://www.mpi-inf.mpg.de/resources/scandb/relrot.tar.gz", data = "http://www.mpi-inf.mpg.de/resources/scandb/", abstract = "Generation and animation of realistic humans is an essential part of many projects in today's media industry. Especially, the games and special effects industry heavily depend on realistic human animation. In this work a unified model that describes both, human pose and body shape is introduced which allows us to accurately model muscle deformations not only as a function of pose but also dependent on the physique of the subject. Coupled with the model's ability to generate arbitrary human body shapes, it severely simplifies the generation of highly realistic character animations. A learning based approach is trained on approximately 550 full body 3D laser scans taken of 114 subjects. Scan registration is performed using a non-rigid deformation technique. Then, a rotation invariant encoding of the acquired exemplars permits the computation of a statistical model that simultaneously encodes pose and body shape. Finally, morphing or generating meshes according to several constraints simultaneously can be achieved by training semantically meaningful regressors.", } @InProceedings{Wang07, author = "J.M. Wang and D.J. Fleet and A. Hertzmann", title = "Gaussian process models for style-content separation", booktitle = "Int. Conf. on Machine learning ({ICML})", year = "2007", pages = "975-982", project = "http://www.dgp.toronto.edu/~jmwang/gpsc/", pdf = "http://www.dgp.toronto.edu/~jmwang/gpsc/gpscpaper.pdf", talk = "http://www.dgp.toronto.edu/~jmwang/movies/icmlTalk.zip", code = "http://www.dgp.toronto.edu/~jmwang/gpsc/mgp.zip", } @Article{Lau09, author = "M. Lau and Z. {Bar-Joseph} and J. Kuffner", title = "Modeling spatial and temporal variation in motion data" journal = "{ACM} Trans. Graph.", volume = "28", number = "5", year = "2009", pdf = "http://graphics.cs.cmu.edu/projects/model_variation/model_variation_sigasia09.pdf", project = "http://graphics.cs.cmu.edu/projects/model_variation/", video = "http://graphics.cs.cmu.edu/projects/model_variation/model_variation_sigasia09.mov", } @InProceedings{Kokkinos07, author = "I. Kokkinos and A. Yuille", title = "Unsupervised learning of object deformation models", booktitle = "ICCV", year = "2007", pdf = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.137.4733&rep=rep1&type=pdf", talk = "http://www.stat.ucla.edu/~jkokkin/Deformations_UCLA_Oct_2007.pdf", } @Article{Anguelov05a, author = "D. Anguelov and P. Srinivasan and D. Koller and S. Thrun and J. Rodgers and J. Davis", title = "Scape: shape completion and animation of people", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2005", project = "http://robotics.stanford.edu/~drago/Projects/scape/scape.html", pdf = "http://robotics.stanford.edu/~drago/Papers/shapecomp.pdf", video = "http://robotics.stanford.edu/~drago/Projects/scape/video.mp4.gz", data = "https://graphics.soe.ucsc.edu/private/data/SCAPE", abstract = "We introduce a data-driven method for building a human shape model that spans variation in both subject shape and pose. The method is based on a representation that incorporates both articulated and nonrigid deformations. We learn a pose deformation model that derives the nonrigid surface deformation as a function of the pose of the articulated skeleton. We also learn a separate model of variation based on body shape. Our two models can be combined to produce 3D surface models with realistic muscle deformation for different people in different poses, when neither appear in the training set. We show how the model can be used for shape completion --- generating a complete surface mesh given a limited set of markers specifying the target shape. We present applications of shape completion to partial view completion and motion capture animation. In particular, our method is capable of constructing a high-quality animated surface model of a moving person, with realistic muscle deformation, using just a single static scan of that person.", } @Article{Allen03, author = "Brett Allen and Brian Curless and Zoran Popovic", title = "The space of human body shapes: reconstruction and parameterization from range scans", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2003", pages = "587-594", project = "http://grail.cs.washington.edu/projects/digital-human", pdf = "http://grail.cs.washington.edu/projects/digital-human/pub/allen03space.pdf", video = "http://grail.cs.washington.edu/projects/digital-human/pub/allen03space.avi", abstract = "We develop a novel method for fitting high-resolution template meshes to detailed human body range scans with sparse 3D markers. We formulate an optimization problem in which the degrees of freedom are an affine transformation at each template vertex. The objective function is a weighted combination of three measures: proximity of transformed vertices to the range data, similarity between neighboring transformations, and proximity of sparse markers at corresponding locations on the template and target surface. We solve for the transformations with a non-linear optimizer, run at two resolutions to speed convergence. We demonstrate reconstruction and consistent parameterization of 250 human body models. With this parameterized set, we explore a variety of applications for human body modeling, including: morphing, texture transfer, statistical analysis of shape, model fitting from sparse markers, feature analysis to modify multiple correlated parameters (such as the weight and height of an individual), and transfer of surface detail and animation controls from a template to fitted models.", } @Article{Praun01, author = "Emil Praun, Wim Sweldens, Peter Schroeder", title = "Consistent Mesh Parameterizations", journal = "Computer Graphics (Proc SIGGRAPH)", year = "2001", pdf = "http://www.cs.princeton.edu/gfx/papers/praun01cmp.pdf", talk = "http://www.cs.princeton.edu/gfx/proj/reparam/reparam.ppt", abstract = "A basic element of Digital Geometry Processing algorithms is the establishment of a smooth parameterization for a given model. In this paper we propose an algorithm which establishes parameterizations for a set of models. The parameterizations are called consistent because they share the same base domain and respect features. They give immediate correspondences between models and allow remeshes with the same connectivity. Such remeshes form the basis for a large class of algorithms, including principal component analysis, wavelet transforms, detail and texture transfer between models, and n-way shape blending. We demonstrate the versatility of our algorithm with a number of examples.", } @Article{Blanz99, author = "V. Blanz and T. Vetter", title = "A morphable model for the synthesis of {3D} faces", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "1999", project = "http://gravis.cs.unibas.ch/Sigg99.html", pdf = "http://gravis.cs.unibas.ch/publications/Sigg99/morphmod2.pdf", video = "http://gravis.cs.unibas.ch/publications/Sigg99/siggraph99.mpg", abstract = "In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an ``unlikely'' appearance. Starting from an example set of 3D face models, we derive a Morphable Face Model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. In this framework, it is easy to control complex facial attributes, such as gender, attractiveness, body weight, or facial expressions. Attributes are automatically learned from a set of faces rated by the user, and can then be applied to classify and manipulate new faces. Given a single photograph of a face, we can estimate its 3D shape, its orientation in space and the illumination conditions in the scene. Starting from a rough estimate of size, orientation and illumination, our algorithm optimizes these parameters along with the face's internal shape and surface colour to find the best match to the input image. The face model extracted from the image can be rotated and manipulated in 3D.", } @Article{Kilian07, author = "M. Kilian and N. Mitra and H. Pottmann", title = "Geometric modeling in shape space", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2007", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Using 3D Model Repositories for Object Recognition and Modeling %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @Article{Xu11, title = "Photo-Inspired Model-Driven 3D Object Modeling", author = "Kai Xu and Hanlin Zheng and Hao Zhang and Daniel Cohen-Orand Ligang Liu and Yueshan Xiong", journal = "{ACM} Trans Graphics (Proc {SIGGRAPH})", year = "2011", abstract = "We introduce an algorithm for 3D object modeling where the user draws creative inspiration from an object captured in a single photograph. Our method leverages the rich source of photographs for creative 3D modeling. However, with only a photo as a guide, creating a 3D model from scratch is a daunting task. We support the modeling process by utilizing an available set of 3D candidate models. Specifically, the user creates a digital 3D model as a geometric variation from a 3D candidate. Our modeling technique consists of two major steps. The first step is a user-guided image-space object segmentation to reveal the structure of the photographed object. The core step is the second one, in which a 3D candidate is automatically deformed to fit the photographed target under the guidance of silhouette correspondence. The set of candidate models have been pre-analyzed to possess useful high-level structural information, which is heavily utilized in both steps to compensate for the ill-posedness of the modeling problems based only on content in a single image. Equally important, the structural information is preserved by the geometric variation so that the final product is coherent with its inherited structural information readily usable for subsequent model refinement or processing.", } @InProceedings{Lai10a, title = "Object Recognition in {3D} Point Clouds Using Web Data and Domain Adaptation", author = "K. Lai and D. Fox", booktitle = "{IJRR}", year = "2010", pdf = "http://www.cs.washington.edu/homes/fox/postscripts/scan-domain-adaptation-ijrr-10.pdf", abstract = "Over the last years, object detection has become a more and more active field of research in robotics. An important problem in object detection is the need for sufficient labeled training data to learn good classifiers. In this paper we show how to significantly reduce the need for manually labeled training data by leveraging data sets available on the World Wide Web. Specifically, we show how to use objects from Google's 3DWarehouse to train an object detection system for 3D point clouds collected by robots navigating through both urban and indoor environments. In order to deal with the different characteristics of the web data and the real robot data, we additionally use a small set of labeled point clouds and perform domain adaptation. Our experiments demonstrate that additional data taken from the 3D Warehouse along with our domain adaptation greatly improves the classification accuracy on real-world environments.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Conditional and Markov Random Fields %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @InProceedings{Agrawal09, author = "Anuraag Agrawal and Atsushi Nakazawa and Haruo Takemura", title = "{MMM}-classification of {3D} range data", booktitle = "International Conference on Robotics and Automation - {ICRA}", year = "2009", page = "2003-2008", pdf = "http://www.lab.ime.cmc.osaka-u.ac.jp/paper/datas/2009/05/Agrawal_0307/Agrawal_200905_paper.pdf", abstract = "This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applications including robot navigation and automation, in particular with respect to path planning. To achieve robust object classification, we propose the idea of an object feature which represents a distribution of neighboring points around a target point. In addition, rather than processing raw points, we reconstruct polygons from the point data, introducing connectivity to the points. With these ideas, we can refine the Markov Random Field (MRF) calculation with more relevant information with regards to determining related points. The algorithm was tested against five outdoor scenes and provided accurate classification even in the presence of many classes of interest.", } @InProceedings{Munoz09a, author = "Daniel Munoz and James A. Bagnell and Nicolas Vandapel and Martial Hebert", title = "Contextual classification with functional Max-Margin Markov Networks", booktitle = "{CVPR}", pages = "975-982", year = "2009", pdf = "http://www.ri.cmu.edu/pub_files/2009/6/munoz_cvpr_09.pdf", talk = "http://www.cs.cmu.edu/~dmunoz/projects/m3n/munoz_cvpr_09_talk.pptx", project = "http://www.cs.cmu.edu/~dmunoz/projects/m3n.html", video = "http://www.youtube.com/v/njg5sZuV7M8", data = "http://www.cs.cmu.edu/~vmr/datasets/oakland_3d/cvpr09/doc/", abstract = "We address the problem of label assignment in computer vision: given a novel 3-D or 2-D scene, we wish to assign a unique label to every site (voxel, pixel, superpixel, etc.). To this end, the Markov Random Field framework has proven to be a model of choice as it uses contextual information to yield improved classification results over locally independent classifiers. In this work we adapt a functional gradient approach for learning high-dimensional parameters of random fields in order to perform discrete, multi-label classification. With this approach we can learn robust models involving high-order interactions better than the previously used learning method. We validate the approach in the context of point cloud classification and improve the state of the art. In addition, we successfully demonstrate the generality of the approach on the challenging vision problem of recovering 3-D geometric surfaces from images.", } @InProceedings{Munoz09b, author = "Daniel Munoz and Nicolas Vandapel and Martial Hebert", title = "Onboard contextual classification of 3-D point clouds with learned high-order Markov Random Fields", booktitle = "International Conference on Robotics and Automation ({ICRA})"", pages = "2009-2016", year = "2009", pdf = "http://www.ri.cmu.edu/pub_files/2009/5/munoz_icra_09.pdf", abstract = "Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performance increase is often at the cost of time consuming, memory intensive learning and slow inference at testing time. Structured prediction for 3-D point cloud classification is one example of such an application. In this paper we present two contributions. First we show how efficient learning of a random field with higher-order cliques can be achieved using subgradient optimization. Second, we present a context approximation using random fields with high-order cliques designed to make this model usable online, onboard a mobile vehicle for environment modeling. We obtained results with the mobile vehicle on a variety of terrains, at 1/3 Hz for a map 25 50 meters and a vehicle speed of 1-2 m/s.", } @InProceedings{Munoz08, author = "Daniel Munoz and Nicolas Vandapel and Martial Hebert", title = "Directional Associative Markov Network for 3-D Point Cloud Classification", year = "2008", booktitle = "{3DPVT}", pdf = "http://www.ri.cmu.edu/pub_files/2008/6/munoz_3dpvt_08.pdf", abstract = "In this paper we address the problem of automated three dimensional point cloud interpretation. This problem is important for various tasks from environment modeling to obstacle avoidance for autonomous robot navigation. In addition to locally extracted features, classifiers need to utilize contextual information in order to perform well. A popular approach to account for context is to utilize the Markov Random Field framework. One recent variant that has successfully been used for the problem considered is the Associative Markov Network (AMN). We extend the AMN model to learn directionality in the clique potentials, resulting in a new anisotropic model that can be efficiently learned using the subgradient method. We validate the proposed approach using data collected from different range sensors and show better performance against standard AMN and Support Vector Machine algorithms.", } @InProceedings{Douillard08, author = "Bertrand Douillard and Dieter Fox and Fabio Ramos", title = "Laser and Vision Based Outdoor Object Mapping", booktitle = "Robotics: Science and Systems ({RSS})", year = "2008", pdf = "http://www.cs.washington.edu/ai/Mobile_Robotics/projects/semantic-mapping/postscripts/crf-mapping-rss-08.pdf", project = "http://www.cs.washington.edu/ai/Mobile_Robotics/projects/semantic-mapping/", video = "http://www.cs.washington.edu/ai/Mobile_Robotics/projects/semantic-mapping/outdoor-objects.avi", abstract = "Generating rich representations of environments is a fundamental task in mobile robotics. In this paper we introduce a novel approach to building object type maps of outdoor environments. Our approach uses conditional random fields (CRF) to jointly classify the laser returns in a 2D scan map into seven object types (car, wall, tree trunk, foliage, person, grass, and other). The spatial connectivity of the CRF is determined via Delaunay triangulation of the laser map. Our model incorporates laser shape features, visual appearance features, visual object detectors trained on existing image data sets and structural information extracted from clusters of laser returns. The parameters of the CRF are trained from partially labeled laser and camera data collected by a car moving through an urban environment. Our approach achieves 91 accuracy in classifying the object types observed along a 3 kilometer long trajectory.", } @InProceedings{Posner08, author = "Ingmar Posner and Mark Cummins and Paul Newman", title = "Fast probabilistic labeling of city maps", booktitle = "Robotics: Science and Systems", year = "2008", pdf = "http://www.roboticsproceedings.org/rss04/p3.pdf", abstract = "This paper introduces a probabilistic, two-stage classification framework for the semantic annotation of urban maps as provided by a mobile robot. During the first stage, local scene properties are considered using a probabilistic bagof- words classifier. The second stage incorporates contextual information across a given scene via a Markov Random Field (MRF). Our approach is driven by data from an onboard camera and 3D laser scanner and uses a combination of appearancebased and geometric features. By framing the classification exercise probabilistically we are able to execute an informationtheoretic bail-out policy when evaluating appearance-based classconditional likelihoods. This efficiency, combined with low order MRFs resulting from our two-stage approach, allows us to generate scene labels at speeds suitable for online deployment and use. We demonstrate and analyze the performance of our technique on data gathered over almost 17 km of track through a city.", } @InProceedings{Chandran-Ramesh07, author = "Manjari {Chandran-Ramesh} and Paul Newman", title = "Assessing Map Quality Using Conditional Random Fields", booktitle = "Field and Service Robotics ({FSR})", pages = "35-48", year = "2007", pdf = "http://hal.inria.fr/docs/00/20/12/16/PDF/fsr_75.pdf", abstract = "This paper is concerned with assessing the quality of work-space maps. While there has been much work in recent years on building maps of field settings, little attention has been given to endowing a machine with introspective competencies which would allow assessing the reliability/plausibility of the representation. We classify regions in 3D point-cloud maps into two binary classes|\plausible or \suspicious. In this paper we concentrate on the classi¯cation of urban maps and use a Conditional Random Fields to model the intrinsic qualities of planar patches and crucially, their relationship to each other. A bipartite labelling of the map is acquired via application of the Graph Cut algorithm. We present results using data gathered by a mobile robot equipped with a 3D laser range sensor while operating in a typical urban setting.", } @InProceedings{Triebel06, author = "Rudolph Triebel and Kristian Kersting and Wolfram Burgard", title = "Robust 3D Scan Point Classification using Associative Markov Networks", booktitle = "Intl. Conf. on Robotics and Automation ({ICRA})", year = "2006", pdf = "http://www.informatik.uni-freiburg.de/~kersting/icra06.pdf", abstract = "In this paper we present an efficient technique tolearn Associative Markov Networks (AMNs) for the segmentation of 3D scan data. Our technique is an extension of the work recently presented by Anguelov et al. [1], in which AMNs are applied and the learning is done using max-margin optimization. In this paper we show that by adaptively reducing the training data, the training process can be performed much more efficiently while still achieving good classification results. The reduction is obtained by utilizing kd-trees and pruning them appropriately. Our algorithm does not require any additional parameters and yields an abstraction of the training data. In experiments with real data collected from a mobile outdoor robot we demonstrate that our approach yields accurate segmentations.", } @InProceedings{Anguelov05b, author = "Dragomir Anguelov and Ben Taskary and Vassil Chatalbashev and Daphne Koller and Dinkar Gupta and Geremy Heitz and Andrew Ng", title = "Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data", booktitle = "{CVPR}", year = "2005", project = "http://ai.stanford.edu/~Drago/Projects/Detector/index.html", video = "http://ai.stanford.edu/~Drago/Projects/Detector/terrain_files/terrain_data/flythru.mp4", data = "http://ai.stanford.edu/~Drago/Projects/Detector/index.html", pdf = "http://ai.stanford.edu/~drago/Papers/amn-detector.pdf", abstract = "We address the problem of segmenting 3D scan data into objects or object classes. Our segmentation framework is based on a subclass of Markov Random Fields (MRFs) which support efficient graph-cut inference. The MRF models incorporate a large set of diverse features and enforce the preference that adjacent scan points have the same classification label. We use a recently proposed maximum-margin framework to discriminatively train the model from a set of labeled scans; as a result we automatically learn the relative importance of the features for the segmentation task.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Mapping of Scenes %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @InProceedings{Lai11b, title = "Sparse Distance Learning for Object Recognition Combining {RGB} and Depth Information", author = "K. Lai and L. Bo and X. Ren and D. Fox", booktitle = "{ICRA}", year = "2011", pdf = "http://ils.intel-research.net/uploads/papers/kevin-RGBD10-sparse-distance.pdf", abstract = "In this work we address joint object category and instance recognition in the context of rapid advances of RGBD (depth) cameras [16, 3]. We study the object recognition problem by collecting a large RGB-D dataset which consists of 31 everyday object categories, 159 object instances and about 100; 000 views of objects with both RGB color and depth. Motivated by local distance learning where elementary distances (over features like SIFT and spin images) can be integrated at a per-view level, we define a view-to-object-instance distance where per-view distances are weighted and merged. We show that the per-instance distance, through jointly learning the perview weights, leads to superior classification performance on object category recognition. More importantly, the per-instance distance allows us to find a sparse solution (through Group- Lasso), where a small subset of representative views of an object are identified and used, and the rest discarded. This not only reduces computational cost but also further increases recognition accuracy. We also empirically compare and validate the use of visual (i.e. RGB) cues and shape (i.e. depth) cues and their combinations.", } @Article{Newman09, author = "Paul Newman and Gabe Sibley and Mike Smith and Mark Cummins and Alastair Harrison and Chris Mei and Ingmar Posner and Robbie Shade and Derik Schröter and Liz Murphy and Winston Churchill and Dave Cole and Ian Reid", title = "Navigating, Recognising and Describing Urban Spaces With Vision and Laser", journal = "International Journal of Robotics Research", month = "July", year = "2009", pdf = "http://www.robots.ox.ac.uk/~mjc/Papers/ijrr_2009_pmn_et_al.pdf", abstract = "In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full sixdegree- of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.", } @InProceedings{Himmelsbach08 author = "M. Himmelsbach and A. M¨uller and T. L¨uttel and H.-J. W¨unsche", title = "LIDAR-based 3D Object Perception", booktitle = "International Workshop on Cognition for Technical Systems", year = "2008", pdf = "http://www.unibw.de/lrt8/forschung/publikationen/2008/himmelsbach_cotesys08_lidarperception", abstract = "This paper describes a LIDAR-based perception system for ground robot mobility, consisting of 3D object detection, classification and tracking. The presented system was demonstrated on-board our autonomous ground vehicle MuCAR-3, enabling it to safely navigate in urban traffic-like scenarios as well as in off-road convoy scenarios. The efficiency of our approach stems from the unique combination of 2D and 3D data processing techniques. Whereas fast segmentation of point clouds into objects is done in a 2 1 2D occupancy grid, classifying the objects is done on raw 3D point clouds. For fast switching of domains, the occupancy grid is enhanced to act like a hash table for retrieval of 3D points. In contrast to most existing work on 3D point cloud classification, where realtime operation is often impossible, this combination allows our system to perform in real-time at 0.1s frame-rate.", } @InProceedings{Posner07, author = "I. Posner and D. Schroeter and P. M. Newman", title = "Describing composite urban workspaces", booktitle = "Intl. Conf. on Robotics and Automation ({ICRA})", year = "2007", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Graphical Models for Object Recognition %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @InProceedings{Endres09, author = "F. Endres and C. Stachniss and W. Burgard and C. Plagemann", title = "Unsupervised Discovery of Object Classes from Range Data using Latent Dirichlet Allocation", booktitle = "Robotics: Science and Systems", year = "2009", pdf = "http://www.roboticsproceedings.org/rss05/p15.pdf", abstract = " Truly versatile robots operating in the real world have to be able to learn about objects and their properties autonomously, that is, without being provided with carefully engineered training data. This paper presents an approach that allows a robot to discover object classes in three-dimensional range data in an unsupervised fashion and without a-priori knowledge about the observed objects. Our approach builds on Latent Dirichlet Allocation (LDA), a recently proposed probabilistic method for discovering topics in text documents. We discuss feature extraction, hypothesis generation, and statistical modeling of objects in 3D range data as well as the novel application of LDA to this domain. Our approach has been implemented and evaluated on real data of complex objects. Practical experiments demonstrate, that our approach is able to learn object class models autonomously that are consistent with the true classifications provided by a human. It furthermore outperforms unsupervised method such as hierarchical clustering that operate on a distance metric.", } %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%% Data Sets %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% @InProceedings{Lai11a, author = "K. Lai and L. Bo and X. Ren and D. Fox", title = "A Large-Scale Hierarchical {RGB-D} Object Dataset", booktitle = "ICRA", year = "2011", project = "http://www.cs.washington.edu/ai/Mobile_Robotics/projects/3d-object-recognition/", data = "http://www.cs.washington.edu/homes/kevinlai/datasets.html", pdf = "http://www.cs.washington.edu/ai/Mobile_Robotics/projects/postscripts/rgbd-dataset-icra-11.pdf", abstract = "Over the last decade, the availability of public image repositories and recognition benchmarks has enabled rapid progress in visual object category and instance detection. Today we are witnessing the birth of a new generation of sensing technologies capable of providing high quality synchronized videos of both color and depth, the RGB-D (Kinectstyle) camera. With its advanced sensing capabilities and the potential for mass adoption, this technology represents an opportunity to dramatically increase robotic object recognition, manipulation, navigation, and interaction capabilities. In this paper, we introduce a large-scale, hierarchical multi-view object dataset collected using an RGB-D camera. The dataset contains 300 objects organized into 51 categories and has been made publicly available to the research community so as to enable rapid progress based on this promising technology. This paper describes the dataset collection procedure and introduces techniques for RGB-D based object recognition and detection, demonstrating that combining color and depth information substantially improves quality of results. ", }