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CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D Point Cloud Understanding (CVPR'22)
Paper Link | Project Page
Citation
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@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}
}
:rocket: News
- (Mar 25, 2023)
- (Mar 2, 2022)
- Paper accepted at CVPR 2022 :tada:
- (Mar 2, 2022)
- Training and evaluation codes for CrossPoint, along with pretrained models are released.
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.