To render RGB and depth images for ShapeNet models, specify the ShapeNetV1 folder here and the path to blender here and here. Then, run
#Rendering chairs, cars and aeroplanes (takes about a day)
cd preprocess/synthetic/rendering
python renderPreprocessShapenet.py
#Rendering chairs, cars and aeroplanes with random translations (takes about a day)
python renderPreprocessShapenetTrans.py
For evaluation and training the 3D-supervised baseline, we need to compute the groun-truth 3D voxelizations. First, modify the path to ShapeNetV1 here and then run
#Computing Gt Voxelizations
cd preprocess/synthetic/voxelization
matlab -nodesktop -nosplash
>> precomputeVoxels