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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
verification ladder T0 review T1 audit T2 compute T3 formal
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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 2 Pith papers

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

  1. 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.

  2. 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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