The main scripts are in the root directory. Execute a script without any arguments to see how the script can be used. Useful scripts are listed below, visit the data page to see the output file formats.
fitAll.py - match final templates to a collection of shapes (final templates can be found in templates/final directory).
createInitTmplt.py - manually create initial template. Press '`' during the execution to see some hotkeys.
learnTmplt.py - learn template (starting with initial template), the output is a final set of templates for a given collection. Note that the working directory (./scratch by default) will contain the directory "workDir/Templates/workDir_GenFinal" which will include fitting of all models to the final template (same as output of fitAll.py).
autoLearnTmplt.py - learn template from several candidate automatic segmentations.
Pre-analyzed datasets and the format description can be found here.
templates - This directory contains initial and final templates. Note that CoSeg are templates learned on Co-Segmentation Benchmark dataset and FC are templates learned on Fuzzy Correspondence dataset. "chair", "plane", "bike", "helicopter" are learned on large datasets from 3D Warehouse (note that *_gtonly templates are learned on a 100-model subset of models with ground truth) see the paper for details.
./scripts/web/viewtemplates.php - visualizes analysis results (note that you will need a web server with php support, also you will need to edit the $dataDir so the directory that contains subdirectories with results).
exportResults.py - exports the analysis results in a simpler format.
correspondences - there is no script that explicitly computes the correspondences (though, App/EvalTemplate.cpp has this code). You can use nearest neighbors of co-aligned points (produced with exportResults.py) to compute correspondences. Or, download matlab figures for error rates on correspondence benchmark presented in the paper.
All results for our paper were produced on a beowulf cluster, using "qsub" command. If qsub is detected this distribution will be executed in parallel. Refer to scripts/largescale/mydb.py for details, especially ExecuteNoWait function.