# RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers

arXiv: https://arxiv.org/abs/2011.11572 · HTML: https://mlsanity.com/paper/2011.11572/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2020.

## Built on by (most cited first, 10 of 10)

- [OmniObject3D: Large-Vocabulary 3D Object Dataset for Realistic Perception, Reconstruction and Generation](https://mlsanity.com/paper/2301.07525/) (2023)
- [Benchmarking and Analyzing Point Cloud Classification under Corruptions](https://mlsanity.com/paper/2202.03377/) (2022)
- [Benchmarking the Robustness of LiDAR Semantic Segmentation Models](https://mlsanity.com/paper/2301.00970/) (2023)
- [Multimodal Shape Completion via IMLE](https://mlsanity.com/paper/2106.16237/) (2021)
- [Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples](https://mlsanity.com/paper/2307.16361/) (2023)
- [CSI: Enhancing the Robustness of 3D Point Cloud Recognition against Corruption](https://mlsanity.com/paper/2310.03360/) (2023)
- [To Supervise or Not to Supervise: Understanding and Addressing the Key Challenges of Point Cloud Transfer Learning](https://mlsanity.com/paper/2403.17869/) (2024)
- [Seeing by haptic glance: reinforcement learning-based 3D object Recognition](https://mlsanity.com/paper/2102.07599/) (2021)
- [Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness](https://mlsanity.com/paper/2207.01548/) (2022)
- [Self-Supervised Learning for Point Clouds Data: A Survey](https://mlsanity.com/paper/2305.11881/) (2023)

## Abstract (arXiv)

The 3D deep learning community has seen significant strides in pointcloud processing over the last few years. However, the datasets on which deep models have been trained have largely remained the same. Most datasets comprise clean, clutter-free pointclouds canonicalized for pose. Models trained on these datasets fail in uninterpretible and unintuitive ways when presented with data that contains transformations "unseen" at train time. While data augmentation enables models to be robust to "previously seen" input transformations, 1) we show that this does not work for unseen transformations during inference, and 2) data augmentation makes it difficult to analyze a model's inherent robustness to transformations. To this end, we create a publicly available dataset for robustness analysis of point cloud classification models (independent of data augmentation) to input transformations, called RobustPointSet. Our experiments indicate that despite all the progress in the point cloud classification, there is no single architecture that consistently performs better -- several fail drastically -- when evaluated on transformed test sets. We also find that robustness to unseen transformations cannot be brought about merely by extensive data augmentation. RobustPointSet can be accessed through https://github.com/AutodeskAILab/RobustPointSet.