Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

28 Jan 2022  ·  Jiachen Sun, Qingzhao Zhang, Bhavya Kailkhura, Zhiding Yu, Chaowei Xiao, Z. Morley Mao ·

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

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ModelNet ImageNet-C
Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
3D Point Cloud Data Augmentation ModelNet40-C PCT+PointCutMix-R Error Rate 0.163 # 1
3D Point Cloud Classification ModelNet40-C PCT+PointCutMix-R Error Rate 0.163 # 2