PointPatchMix: Point Cloud Mixing with Patch Scoring

AAAI Conference on Artificial Intelligence (AAAI) 2024

We propose PointPatchMix, a novel point cloud mixing method that operates at the patch level and integrates a patch scoring module. You can check out paper for more details.

The overall scheme of the PointPatchMix. (a) The original point clouds are divided into multiple patches, subsequently undergoing mask token processing and mixing. (b) A pre-trained teacher model assigns each patch with a content-based significance score. The ground truth of the mixed point cloud is ascertained by aggregating the scores of designated patches.

Qualitative examples

For each set of samples, the mixed point cloud (right) consists of blue and red patches, where the blue patches are randomly selected from the left object and the red patches are the corresponding complementary part from the middle object. At the bottom of the point clouds, the blue and red numbers represent the scores of these patches, while the black numbers are the percentage of the number of points.

Installation

PyTorch >= 1.7.0 < 1.11.0; python >= 3.7; CUDA >= 9.0; GCC >= 4.9; torchvision;

pip install -r requirements.txt

# EMD
cd ./emd
TORCH_CUDA_ARCH_LIST="3.5;5.0;6.0;6.1;7.0;7.5+PTX" python setup.py install --user
# PointNet++
pip install "git+https://github.com/erikwijmans/Pointnet2_PyTorch.git#egg=pointnet2_ops&subdirectory=pointnet2_ops_lib"
# GPU kNN
pip install --upgrade https://github.com/unlimblue/KNN_CUDA/releases/download/0.2/KNN_CUDA-0.2-py3-none-any.whl

Datasets

We conduct extensive experiments on both synthetic and real-world datasets in point cloud shape classification to evaluate the effectiveness of PointPatchMix, i.e., ModelNet40 and ScanObjetNN.

Please download the datasets from DATASET.md in Point-MAE.

Then, we adopt Point-MAE as our teacher model to generate the patch token scores and save the scores offline to improve training efficiency.

Experiments

Train Point-MAE on ModelNet40, run:

CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/patchmix_modelnet40_pointmae.yaml \
--scratch_model --score --exp_name <output_file_name>

Train Point-MAE on ScanObjectNN, run:

CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/patchmix_scanobjectnnhard_pointmae.yaml \
--scratch_model --score --exp_name <output_file_name>

Finetune Point-MAE on ModelNet40, run:

CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/patchmix_modelnet40_pointmae.yaml \
--finetune_model --score --exp_name <output_file_name> --ckpts <path/to/pre-trained/model>

If you want to try different models or datasets, first create a new config file, and pass its path to --config.

CUDA_VISIBLE_DEVICES=<GPU> python main.py --config cfgs/<config_file> \
--scratch_model --score --exp_name <output_file_name>

Acknowledgements

Our codes are built upon Point-MAE and PointCutMix.

Citation

If you find our code helpful, please cite our paper:

@inproceedings{wang2024pointpatchmix,
  title={PointPatchMix: Point Cloud Mixing with Patch Scoring},
  author={Wang, Yi and Wang, Jiaze and Li, Jinpeng and Zhao, Zixu and Chen, Guangyong and Liu, Anfeng and Heng, Pheng-Ann},
  journal={Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI-24)},
  year={2024}
}

winfred2027/PointPatchMix