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MohamedAfham / CrossPoint

Official implementation of "CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding" (CVPR, 2022)
https://mohamedafham.github.io/CrossPoint/
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3d-point-clouds cross-modal-learning deep-learning few-shot-learning object-classification point-cloud self-supervised-learning transfer-learning unsupervised-learning

readme

CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22)

Paper Link | Project Page

Citation

If you find our work, this repository, or pretrained models useful, please consider giving a star ⭐ and citation.

@InProceedings{Afham_2022_CVPR,
    author    = {Afham, Mohamed and Dissanayake, Isuru and Dissanayake, Dinithi and Dharmasiri, Amaya and Thilakarathna, Kanchana and Rodrigo, Ranga},
    title     = {CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {9902-9912}
}

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Dependencies

Refer requirements.txt for the required packages.

Pretrained Models

CrossPoint pretrained models with DGCNN feature extractor are available here.

Download data

Datasets are available here. Run the command below to download all the datasets (ShapeNetRender, ModelNet40, ScanObjectNN, ShapeNetPart) to reproduce the results.

cd data
source download_data.sh

Train CrossPoint

Refer scripts/script.sh for the commands to train CrossPoint.

Downstream Tasks

1. 3D Object Classification

Run eval_ssl.ipynb notebook to perform linear SVM object classification in both ModelNet40 and ScanObjectNN datasets.

2. Few-Shot Object Classification

Refer scripts/fsl_script.sh to perform few-shot object classification.

3. 3D Object Part Segmentation

Refer scripts/script.sh for fine-tuning experiment for part segmentation in ShapeNetPart dataset.

Acknowledgements

Our code borrows heavily from DGCNN repository. We thank the authors of DGCNN for releasing their code. If you use our model, please consider citing them as well.