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.
@article{arxiv.2112.12053,
title = {Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results},
author = {Liang Pan and Tong Wu and Zhongang Cai and Ziwei Liu and Xumin Yu and Yongming Rao and Jiwen Lu and Jie Zhou and Mingye Xu and Xiaoyuan Luo and Kexue Fu and Peng Gao and Manning Wang and Yali Wang and Yu Qiao and Junsheng Zhou and Xin Wen and Peng Xiang and Yu-Shen Liu and Zhizhong Han and Yuanjie Yan and Junyi An and Lifa Zhu and Changwei Lin and Dongrui Liu and Xin Li and Francisco Gómez-Fernández and Qinlong Wang and Yang Yang},
journal= {arXiv preprint arXiv:2112.12053},
year = {2021}
}