Enhancing Sampling Protocol for Point Cloud Classification Against Corruptions

2024-08-22

AI Summary

Existing point cloud sampling methods (e.g., FPS, FSS) exhibit poor robustness under realistic corruptions such as sensor noise, leading to substantial degradation in classification accuracy and limiting deployment in safety-critical applications like autonomous driving. To address this, we propose PointSP—a lightweight, plug-and-play, non-learning-based sampling enhancement protocol. PointSP introduces a novel keypoint reweighting mechanism coupled with a local-global adaptive downsampling strategy, requiring no additional training and remaining fully compatible with arbitrary network architectures. Furthermore, it incorporates tangent-plane interpolation to preserve local geometric structure while improving point density uniformity. Extensive experiments on multiple synthetic and real-world noisy datasets demonstrate that PointSP consistently outperforms state-of-the-art methods, achieving significant gains in both classification robustness and accuracy.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Data and user privacy-enhancing technologies for the Web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been relied upon. However, real-world data often suffer from corruptions, such as sensor noise, which violates the benign data assumption in current protocols. As a result, these protocols are highly vulnerable to noise, posing significant safety risks in critical applications like autonomous driving. To address these issues, we propose an enhanced point cloud sampling protocol, PointSP, designed to improve robustness against point cloud corruptions. PointSP incorporates key point reweighting to mitigate outlier sensitivity and ensure the selection of representative points. It also introduces a local-global balanced downsampling strategy, which allows for scalable and adaptive sampling while maintaining geometric consistency. Additionally, a lightweight tangent plane interpolation method is used to preserve local geometry while enhancing the density of the point cloud. Unlike learning-based approaches that require additional model training, PointSP is architecture-agnostic, requiring no extra learning or modification to the network. This enables seamless integration into existing pipelines. Extensive experiments on synthetic and real-world corrupted datasets show that PointSP significantly improves the robustness and accuracy of point cloud classification, outperforming state-of-the-art methods across multiple benchmarks.

Problem

• Point Cloud Sampling
• Noise Robustness
• Autonomous Driving

Innovation

• PointSP
• Noise Resistance
• Point Cloud Classification