Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions

2022/01/28 by J. F. Sun, Qingzhao Zhang, Sun, Jiachen +9 · 9 citations
Computer Science · Earth and Planetary Sciences · Engineering · #3D Surveying and Cultural Heritage #Advanced Neural Network Applications #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2201.12296

openalex publication_date 2022/01/28 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28

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

Cited by

• Adapt-As-You-Walk Through the Clouds: Training-Free Online Test-Time Adaptation of 3D Vision-Language Foundation Models
• TACO-Net: Topological Signatures Triumph in 3D Object Classification
• Skeleton-based Robust Registration Framework for Corrupted 3D Point Clouds
• Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training
• Joint graph entropy knowledge distillation for point cloud classification and robustness against corruptions
• Purge-Gate: Backpropagation-Free Test-Time Adaptation for Point Clouds Classification via Token Purging
• Deep Learning For Point Cloud Denoising: A Survey
• ModelNet40-E: An Uncertainty-Aware Benchmark for Point Cloud Classification
• 3D Test-time Adaptation via Graph Spectral Driven Point Shift

Related

• Benchmarking and Analyzing Point Cloud Classification under Corruptions
• Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples
• CSI: Enhancing the Robustness of 3D Point Cloud Recognition against Corruption
• RobustPointSet: A Dataset for Benchmarking Robustness of Point Cloud Classifiers
• Desensitizing for Improving Corruption Robustness in Point Cloud Classification through Adversarial Training