Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been recognized and utilized. However, real-world data often suffer from corrputions such as sensor noise, which violates the benignness assumption of point cloud in current protocols. Consequently, they are notably 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, PointDR, which comprises two components: 1) Downsampling for key point identification and 2) Resampling for flexible sample size. Furthermore, differentiated strategies are implemented for training and inference processes. Particularly, an isolation-rated weight considering local density is designed for the downsampling method, assisting it in performing random key points selection in the training phase and bypassing noise in the inference phase. A local-geometry-preserved upsampling is incorporated into resampling, facilitating it to maintain a stochastic sample size in the training stage and complete insufficient data in the inference. It is crucial to note that the proposed protocol is free of model architecture altering and extra learning, thus minimal efforts are demanded for its replacement of the existing one. Despite the simplicity, it substantially improves the robustness of point cloud learning, showcased by outperforming the state-of-the-art methods on multiple benchmarks of corrupted point cloud classification. The code will be available upon the paper's acceptance.

本文解决了现有点云学习中的采样协议在面对传感器噪声等数据污染时的脆弱性问题。通过提出一种增强的点云采样协议PointDR，该方法结合了关键点识别和灵活样本大小的下采样与重采样策略，显著提升了点云学习的鲁棒性，尤其在受污染的点云分类任务中表现优于现有技术。

增强采样协议以提升点云分类的鲁棒性