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Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

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arxiv 2112.12053 v1 pith:SI5ZTDZ7 submitted 2021-12-22 cs.CV

Liang Pan , Tong Wu , Zhongang Cai , Ziwei Liu , Xumin Yu , Yongming Rao , Jiwen Lu , Jie Zhou
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Mingye Xu Xiaoyuan Luo Kexue Fu Peng Gao Manning Wang Yali Wang Yu Qiao Junsheng Zhou Xin Wen Peng Xiang Yu-Shen Liu Zhizhong Han Yuanjie Yan Junyi An Lifa Zhu Changwei Lin Dongrui Liu Xin Li Francisco Gómez-Fernández Qinlong Wang Yang Yang
This is my paper · ORCID
classification cs.CV
keywords pointcloudpartialcompletionmulti-viewregistrationbecomeschallenge
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As real-scanned point clouds are mostly partial due to occlusions and viewpoints, reconstructing complete 3D shapes based on incomplete observations becomes a fundamental problem for computer vision. With a single incomplete point cloud, it becomes the partial point cloud completion problem. Given multiple different observations, 3D reconstruction can be addressed by performing partial-to-partial point cloud registration. Recently, a large-scale Multi-View Partial (MVP) point cloud dataset has been released, which consists of over 100,000 high-quality virtual-scanned partial point clouds. Based on the MVP dataset, this paper reports methods and results in the Multi-View Partial Point Cloud Challenge 2021 on Completion and Registration. In total, 128 participants registered for the competition, and 31 teams made valid submissions. The top-ranked solutions will be analyzed, and then we will discuss future research directions.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Measuring Uncertainty in Shape Completion to Improve Grasp Quality

    cs.RO 2025-04 reject novelty 6.0 of 10

    Accounting for shape-completion uncertainty in the grasp quality score improves the success rate of the top five ranked grasps in table-top robot experiments.

  2. SUMI: Scalable Unified Model for 3D Point Cloud Inference

    cs.CV 2026-08 conditional novelty 5.0 of 10

    SUMI refines coarse 3D point cloud predictions by injecting noisy geometric features into cross-attention, and reports state-of-the-art Chamfer distance on PCN, ShapeNet-55/34, and MVP.

  3. Curvature Informed Furthest Point Sampling

    cs.CV 2024-11 reject novelty 4.0 of 10

    CFPS claims to improve task accuracy by swapping low-curvature points in an FPS set with high-curvature points using a learned exchange ratio, but the reported results are internally inconsistent.

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