Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions (2201.12296v1)

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

• The paper introduces ModelNet40-C, a systematic benchmark that assesses 3D models against 75 corruption types, revealing a performance drop exceeding 300% on corrupted data.
• The study evaluates several state-of-the-art models and finds that Transformer-based architectures better withstand occlusion and rotation corruptions compared to traditional networks like PointNet.
• The research demonstrates that augmentation and test-time adaptation methods, such as TENT, can significantly enhance model robustness for real-world, safety-critical applications.

Overview of "Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions"

The paper "Benchmarking Robustness of 3D Point Cloud Recognition Against Common Corruptions" tackles the increasingly critical issue of robustness in 3D point cloud processing, especially as 3D data becomes central to many safety-critical applications, such as autonomous driving and robotics. Despite the advances in 3D deep learning architectures, little attention has been given to robustness against real-world corruptions. This work introduces a new benchmark, ModelNet40-C, illuminating the robustness of state-of-the-art (SOTA) 3D models under 15 diverse corruption conditions.

Contributions and Core Findings

The authors offer several significant contributions:

1. ModelNet40-C Benchmark: This is the first systematic benchmark designed to assess the robustness of 3D point cloud recognition models. It includes 75 corruptions, categorically organized into density, noise, and transformation-based alterations at varying severity levels.
2. Performance Gap Analysis: The paper reveals a notable performance drop of over 300% when models trained on the original ModelNet40 are tested on the corrupted dataset, indicating that high accuracy on clean data does not translate to robustness.
3. Robustness Strategies: Through experimentation with multiple models, including PointNet, PointNet++, DGCNN, RSCNN, PCT, and SimpleView, the paper identifies significant insights about robustness in the face of corruptions. Key insights include the vulnerability to occlusion and rotation corruptions, with Transformer-like architectures showing comparatively better resilience.
4. Augmentation and Adaptation Techniques: The paper evaluates several augmentation strategies (e.g., PointCutMix, PointMixup, RSMix), noting that while these can help, test-time adaptation methods like TENT show particular promise in addressing harder-to-handle corruptions.

Detailed Analysis

The in-depth analysis makes several noteworthy technical claims:

• Model-Specific Vulnerabilities and Strengths: Different architectures exhibit specific weaknesses, such as PointNet's vulnerability to transformations. In contrast, Transform-based architectures like PCT generally withstand transformations better.
• Augmentation Efficiency: Data augmentation strategies can enhance model robustness against corruptions, with specific methods outperforming others on particular corruption types. For instance, PointCutMix-R demonstrates potential in handling noise-based corruptions effectively.
• Adaptation Superiority in Hard Corruptions: Test-time adaptation strategies, although not uniformly superior to augmentations, show marked improvements against complex corruptions like rotation and occlusion, suggesting further refinement and incorporation in future systems.

Implications and Future Directions

The findings underscore the necessity of comprehensive robustness evaluation for 3D models, which is indispensable for deploying these systems in real-world, unregulated environments. The insights gained point toward the need for continued innovation in robustness-centric architecture design, particularly emphasizing transformer-based designs that show promising results.

The paper also opens avenues for future research into more robust training recipes and test-time strategies. Furthermore, extending this evaluation framework to more complex 3D tasks (e.g., semantic segmentation and object detection) represents a logical next step.

In conclusion, the introduction of ModelNet40-C sets a new standard in evaluating and understanding the robustness of 3D point cloud models. The paper not only highlights prevalent vulnerabilities but also provides a critical starting point for future innovations in the field of robust 3D vision systems. This contribution will undoubtedly be instrumental as autonomous systems become more entrenched in everyday applications, where robustness is non-negotiable.