论文标题
基准测试和分析腐败下的点云分类
Benchmarking and Analyzing Point Cloud Classification under Corruptions
论文作者
论文摘要
3D感知，尤其是点云分类，取得了重大进展。但是，在现实世界的部署中，由于场景的复杂性，传感器的不准确和处理不精确性，点云腐败是不可避免的。在这项工作中，我们的目标是严格基准和分析腐败下的点云分类。为了进行系统的调查，我们首先提供了共同3D腐败的分类法，并确定原子腐败。然后，我们对广泛的代表点云模型进行全面评估，以了解其鲁棒性和概括性。我们的基准结果表明，尽管点云分类性能随着时间的推移而有所提高，但最新的方法却越来越不健壮。基于获得的观察结果，我们提出了几种有效的技术来增强点云分类器的鲁棒性。我们希望我们的全面基准，深入分析和提出的技术能够以强大的3D感知激发未来的研究。
3D perception, especially point cloud classification, has achieved substantial progress. However, in real-world deployment, point cloud corruptions are inevitable due to the scene complexity, sensor inaccuracy, and processing imprecision. In this work, we aim to rigorously benchmark and analyze point cloud classification under corruptions. To conduct a systematic investigation, we first provide a taxonomy of common 3D corruptions and identify the atomic corruptions. Then, we perform a comprehensive evaluation on a wide range of representative point cloud models to understand their robustness and generalizability. Our benchmark results show that although point cloud classification performance improves over time, the state-of-the-art methods are on the verge of being less robust. Based on the obtained observations, we propose several effective techniques to enhance point cloud classifier robustness. We hope our comprehensive benchmark, in-depth analysis, and proposed techniques could spark future research in robust 3D perception.
