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.

翻译：近来，借助众多数据集，三维点云分类取得了显著进展。然而，这些数据集并未反映真实场景中点云因遮挡导致的不完整性，这限制了现有方法的实际应用。为弥补这一空白，我们提出ModelNet-O，这是一个包含123,041个样本的大规模合成数据集，模拟了单目相机扫描产生的自遮挡真实点云。ModelNet-O比现有数据集大10倍，并提供了更具挑战性的案例以评估现有方法的鲁棒性。对ModelNet-O的观察表明，精心设计的稀疏结构能够保留点云在遮挡下的结构信息，这启发我们提出一种鲁棒的点云处理方法，该方法在多层级中采用关键点采样（CPS）策略。我们将该方法命名为PointMLS。通过大量实验，我们证明PointMLS在ModelNet-O上取得了最先进的结果，并在常规数据集上取得了具有竞争力的结果，且该方法鲁棒且有效。更多实验也进一步验证了PointMLS的鲁棒性和有效性。