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Boostrap

  1. Move all shapenet models into the 'shapenet' folder, you might need to make it
# structure shouild look like this:
./render_shapenet_data/shapenet/03790512/1a2d2208f73d0531cec33e62192b66e5/model_normalized.obj
  1. Run render_all.py, no args
python render_all.py

Tested on blender 3.3

Render Shapenet Dataset

  • Download shapenet V1 dataset following the official link and unzip the downloaded file unzip SHAPENET_SYNSET_ID.zip.
  • Download Blender following the official link, we used Blender v2.90.0, we haven't tested on other versions.
  • Install required libraries:
apt-get install -y libxi6 libgconf-2-4 libfontconfig1 libxrender1
cd BLENDER_PATH/2.90/python/bin
./python3.7m -m ensurepip
./python3.7m -m pip install numpy
  • Running the render script:
python render_all.py --save_folder PATH_TO_SAVE_IMAGE --dataset_folder PATH_TO_3D_OBJ --blender_root PATH_TO_BLENDER

  • (Optional) The code will save the output from blender to tmp.out, this is not necessary for training, and can be removed by rm -rf tmp.out

  • This code is adopted from this GitHub repo, we thank the author for sharing the codes!