Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

• URL: http://arxiv.org/abs/2201.12296v1
• Date: Fri, 28 Jan 2022 18:01:42 GMT
• Title: Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions
• Authors: Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, and Z. Morley Mao
• Abstract summary: We present ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness. Our evaluation shows a significant gap between the performances on ModelNet40 and ModelNet40-C for state-of-the-art (SOTA) models. We unveil that Transformer-based architectures with proper training recipes achieve the strongest robustness.
• Score: 38.89370166717221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks on 3D point cloud data have been widely used in the real world, especially in safety-critical applications. However, their robustness against corruptions is less studied. In this paper, we present ModelNet40-C, the first comprehensive benchmark on 3D point cloud corruption robustness, consisting of 15 common and realistic corruptions. Our evaluation shows a significant gap between the performances on ModelNet40 and ModelNet40-C for state-of-the-art (SOTA) models. To reduce the gap, we propose a simple but effective method by combining PointCutMix-R and TENT after evaluating a wide range of augmentation and test-time adaptation strategies. We identify a number of critical insights for future studies on corruption robustness in point cloud recognition. For instance, we unveil that Transformer-based architectures with proper training recipes achieve the strongest robustness. We hope our in-depth analysis will motivate the development of robust training strategies or architecture designs in the 3D point cloud domain. Our codebase and dataset are included in https://github.com/jiachens/ModelNet40-C

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