PointCloud-C

Overview

3D perception, especially point cloud classification and part segmentation, has achieved substantial progress. However, in real-world deployment, point cloud corruptions are inevitable due to the scene complexity, sensor inaccuracy, and processing imprecision. In this work, we contribute ModelNet-C and ShapeNet-C, aiming at rigorously benchmarking and analyzing point cloud robustness under various real-world corruptions.

News

2022.11 - The preprint of the PointCloud-C paper is available here.
2022.11 - We have successfully hosted the 1st PointCloud-C challenge! Winners: 🥇 Antins_cv, 🥈 DGPC & DGPS, and 🥉 BIT_gdxy_xtf.
2022.07 - Competition starts! Join now at our CodaLab page.
2022.06 - We release the benchmarking toolkit on GitHub. 🐈
2022.06 - PointCloud-C is now live on Paper-with-Code. Join the benchmark today!
2022.06 - The 1st PointCloud-C challenge will be hosted in conjecture with the ECCV'22 SenseHuman workshop. 🚀
2022.06 - We are organizing the 1st PointCloud-C challenge! Click here to explore the competition details.
2022.05 - ModelNet-C is accepted to ICML 2022. Code is available on GitHub. See here for more details. 🎉

Basic Statistics

Scale & Features

• The very first test-suite for point cloud robustness analysis under corruptions.
• Two sets: ModelNet-C for point cloud classification and ShapeNet-C for part segmentation.
• Real-world corruption sources, ranging from object-, senor-, and processing-levels.
• Seven types of corruptions, each with five severity levels.

Taxonomy

We unify the corruption taxonomy into seven fundamental atomic corruptions: “Add Global”, “Add Local”, “Drop Global”, “Drop Local”, “Rotate”, “Scale” and “Jitter”. Consequently, each real-world corruption is broken down into a combination of the atomic corruptions. In addition, we set five severity levels for each corruption, based on which we randomly sample from the atomic operations to form a composite corruption test set. The detailed description and implementation can be found in our papers.

Benchmark

Based on our test suite, we benchmark more than 20 state-of-the-art methods for point cloud classification and part segmentation, including architecture design, augmentations, and self-supervised pretrains.

Corruption Taxonomy

We break down common corruptions into detailed corruption sources on object-, senor-, and processing-levels, which are further simplified into a combination of seven atomic corruptions for a more controllable empirical analysis. More details are available here.

Corruption Studies

Citation

If you find our work useful for your research, please consider citing the following papers:

@article{ren2022pointcloud-c,
  title={Benchmarking and Analyzing Point Cloud Robustness under Corruptions},
  author={Jiawei Ren and Lingdong Kong and Liang Pan and Ziwei Liu},
  journal={arXiv:220x.xxxxx},
  year={2022}
}

@article{ren2022modelnet-c,
  title={Benchmarking and Analyzing Point Cloud Classification under Corruptions},
  author={Jiawei Ren and Liang Pan and Ziwei Liu},
  journal={International Conference on Machine Learning (ICML)},
  year={2022}
}

Contact

Any feedback is very welcome! Please contact us at [email protected] and [email protected].