This repository propose python scripts for point cloud classification and segmentation. The library is coded with PyTorch.

A preprint of the paper can be found on Arxiv:
http://arxiv.org/abs/1904.02375

Code is released under dual license depending on applications, research or commercial. Reseach license is GPLv3. See the license.

If you use this code in your research, please consider citing: (citation will be updated as soon as 3DOR proceedings will be released)

@inproceedings{boulch2019,
  title={Generalizing discrete convolutions for unstructured point clouds},
  author={Boulch, Alexandre},
  booktitle={Eurographics Workshop on 3D Object Retrieval},
  year={2019}
}

• Pytorch
• Scikit-learn for confusion matrix computation, and efficient neighbors search
• Trimesh (for Modelnet40) for loading triangular meshes and sampling points
• TQDM for progress bars
• PlyFile

All these dependencies can be install via conda in an Anaconda environment or via pip.

We propose scripts for training on several point cloud datasets:

• ModelNet40 (meshes can be found here). The meshes are sampled in the code using Trimesh.
• ShapeNet (code to be added)
• S3DIS (code to be added)
• Semantic8 (code to added)

python modelnet_classif.py --rootdir path_to_modelnet40_data

For testing with one tree per shape:

python modelnet_classif.py --rootdir path_to_modelnet40_data --savedir path_to_statedict_directory --test

For testing with more than one tree per shape: (this code is not optimized at all and is very slow)

python modelnet_classif.py --rootdir path_to_modelnet40_data --savedir path_to_statedict_directory --test --ntree 2

The script for downloading and preparing the point clouds are from PointCNN repository https://github.com/yangyanli/PointCNN[](https://github.com/yangyanli/PointCNN). They are in the data_conversions folder.

python3 ./download_datasets.py -d shapenet_partseg -f path_to_directory
python3 ./prepare_partseg_data.py -f path_to_shapenet_partseg

The training script is shapenet_seg.py:

python shapenet_seg.py --savedir path_to_save_directory --rootdir path_to_shapenet_partseg

• Inference

Testing is a two-step process. First, inference with shapenet_seg.py script:

python shapenet_seg.py --savedir path_to_model_directory  --rootdir path_to_shapenet_partseg --test

You can also use the --ply flag to generate PLY file for result visualization.

The previous command line is result with one spatial tree. To test with multiple spatial trees, use the --ntree flag:

python shapenet_seg.py --savedir path_to_model_directory  --rootdir path_to_shapenet_partseg --test --ntree 4

• Scores

The scores computation scripts are also adapted from PointCNN repository https://github.com/yangyanli/PointCNN[](https://github.com/yangyanli/PointCNN).

python shapenet_seg_eval.py --rootdir path_to_shapenet_partseg --preddir path_to_predictions

You can also compute the part average scores using the flag --part_av.

Code to be released

Code to be released