In this project, we perform a detailed empirical study of 3d shape classification under few-shot setting using common 3D architectures, datasets and few-shot techniques.

• Python3
• Pytorch
• json

• Change directory to ./filelists/ModelNet40_voxels
• download and unzip ModelNet40.zip from (https://modelnet.cs.princeton.edu/)
• use utils/binvox_convert.py to convert this data to voxel format

• Change directory to ./filelists/ModelNet40_views
• download and unzip rendered images from (https://github.com/jongchyisu/mvcnn_pytorch)

• Change directory to ./filelists/ModelNet40_points
• download and unzip data from (https://github.com/yanx27/Pointnet_Pointnet2_pytorch)

• Require three data split json file: 'base.json', 'val.json', 'novel.json' for each dataset
• The format should follow
  {"label_names": ["class0","class1",...], "image_names": ["filepath1","filepath2",...],"image_labels":[l1,l2,l3,...]}
• See utils/create_json.ipynb on how to generate these files for ModelNet40 dataset. Update data_dir['DATASETNAME'] in configs.py.

In general, run python ./train.py --dataset [DATASETNAME] --model [BACKBONENAME] --method [METHODNAME] [--OPTIONARG]

Specifically, below are some examples to run experiments on ModelNet40 dataset using different architectures and few-shot techniques:

• VoxNet: python ./train.py --dataset modelnet40_voxels --method protonet --voxelized
• MVCNN: python ./train.py --dataset modelnet40_views --model Conv4 --method maml --num_views 12
• PointNet: python ./train.py --dataset modelnet40_points --method baseline --num_points 1024 Similarly, you can use other 3D datasets. Please refer to io_utils.py for additional options.

We have modified and built upon the following publicly available code:

• Few-shot Framework and Methods: https://github.com/wyharveychen/CloserLookFewShot
• VoxNet: https://github.com/dimatura/voxnet; https://github.com/MonteYang/VoxNet.pytorch; https://github.com/Ryanglambert/3d_model_retriever
• MVCNN: https://github.com/jongchyisu/mvcnn_pytorch; https://github.com/RBirkeland/MVCNN-PyTorch
• PointNet: https://github.com/charlesq34/pointnet; https://github.com/fxia22/pointnet.pytorch; https://github.com/yanx27/Pointnet_Pointnet2_pytorch