Data
We would like to thank the 3D Warehouse[1], Wang et al.[2], and the users of SketchUp
for making these models available. Before you start, take a look at this
pre-analyzed example to see what the analyzed results look like (also, see the format description).
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Data Format
Getting the models and the ground truth.
Downloading the 3D Warehouse models
We do not yet have a permission from 3D Warehouse to distribute the models
(we are trying to find a solution with the SketchUp team). For now, you
can obtain the models by: (1) downloading the *.skp files, (2) convert them
to *.off. Note that the second step is complicated because we could not find
any command-line converters for *skp files. Thus, our solution
is to run a Ruby script from SketchUp, convert files to google earth / collada format,
and then convert it further to *off using open collada.
Here are the scripts:
- Step 1: Download scripts
- Step 2: Download SketchUp
- Step 3: Download models by executing DownloadSkp.py. Note that ids/*txt files contain
a map between our *_export.tgz and 3D warehouse model ids.
- Step 4: Convert SketchUp to Google Earth format with ConvertSkp2Kmz.py
(this step requires SketchUp and MacOSX)
- Step 5: Convert Google Earth format to OFF with ConvertKmz2Off.py
(this step requires 64-bit MacOSX or Linux).
Ground Truth
- _gt.zip contains ground truth data, where each 3D model has a corresponding text file.
Only consider models where the first line says "Valid".
It is followed by feature points, one point per line, ordered consistently across all
models. Each line [tid b1 b2 b3 x y z] starts with barycentric coordinates (triangle id
+ 3 coordinates), and ends with 3 coordinate positions of the feature point.
Lines with -1 for triangle id indicate that there is no feature point.
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Results format
Archives in the second column contain analysis results that are simplified to
make parsing easier.
_export.tgz contains analysis of ALL models.
_gt_export.tgz contains analysis of 100 models that have ground truth
correspondences.
Here is the content description for these directories.
_scores.txt Lists models along with analysis meta-data. Each line has the following
entries: model_id fitting_energy computation_time template_id 0. You can
pick models with the smallest fitting_energy to ensure that you only have positive
results (e.g. for an application).
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