ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point
  Cloud Classification
Zhongbin Fang Xia Li Xiangtai Li Shen Zhao Mengyuan Liu
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.