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。

ModelNet-O：一个适用于遮挡感知点云分类的大规模合成数据集