ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification

• URL: http://arxiv.org/abs/2401.08210v1
• Date: Tue, 16 Jan 2024 08:54:21 GMT
• Title: ModelNet-O: A Large-Scale Synthetic Dataset for Occlusion-Aware Point Cloud Classification
• Authors: Zhongbin Fang, Xia Li, Xiangtai Li, Shen Zhao, Mengyuan Liu
• Abstract summary: We propose ModelNet-O, a large-scale synthetic dataset of 123,041 samples. ModelNet-O emulates real-world point clouds with self-occlusion caused by scanning from monocular cameras. We propose a robust point cloud processing method called PointMLS.
• Score: 28.05358017259757
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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