Recently, 3D point cloud classification has made significant progress with
the help of many datasets. However, these datasets do not reflect the
incomplete nature of real-world point clouds caused by occlusion, which limits
the practical application of current methods. To bridge this gap, we propose
ModelNet-O, a large-scale synthetic dataset of 123,041 samples that emulate
real-world point clouds with self-occlusion caused by scanning from monocular
cameras. ModelNet-O is 10 times larger than existing datasets and offers more
challenging cases to evaluate the robustness of existing methods. Our
observation on ModelNet-O reveals that well-designed sparse structures can
preserve structural information of point clouds under occlusion, motivating us
to propose a robust point cloud processing method that leverages a critical
point sampling (CPS) strategy in a multi-level manner. We term our method
PointMLS. Through extensive experiments, we demonstrate that our PointMLS
achieves state-of-the-art results on ModelNet-O and competitive results on
regular datasets, and it is robust and effective. More experiments also
demonstrate the robustness and effectiveness of PointMLS.

通过模拟真实世界中因单目摄像头扫描而导致的自遮挡，我们提出了一种名为 PointMLS 的鲁棒点云处理方法，通过 ModelNet-O 数据集的实验结果证明了该方法在点云分类中取得了最先进的结果，并且在常规数据集上取得了有竞争力的结果，而且具有鲁棒性和有效性。