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

2021-12-22 · Liang Pan, Tong Wu, Zhongang Cai, Ziwei Liu, Xumin Yu, Yongming Rao, Jiwen Lu, Jie zhou, 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

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.

📄 PDF Abstract BibTeX arXiv:2112.12053

Code (2)

paul007pl/MVP_Benchmark 공식 구현 pytorch

paul007pl/VRCNet pytorch

Tasks

3D Reconstruction Point Cloud Completion Point Cloud Registrationvalid

Self-Supervised Point Cloud Completion based on Multi-View Augmentations of Single Partial Point Cloud

2025-09-26 · Jingjing Lu, Huilong Pi, Yunchuan Qin, Zhuo Tang 외 arxiv

Point cloud completion aims to reconstruct complete shapes from partial observations. Although current methods have achieved remarkable performance, they still have some limitations: Supervised methods heavily rely on gr…

Point Cloud Completion Point Clouds

Multiview Based 3D Scene Understanding On Partial Point Sets

2018-11-30 · Ye Zhu, Sven Ewan Shepstone, Pablo Martínez-Nuevo, Miklas Strøm Kristoffersen 외

Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recog…

3D Part Segmentation 3D Shape Recognition Scene Understanding Segmentation+1

Variational Relational Point Completion Network

2021-04-20 · CVPR 2021 1 · Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang 외

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise. Existing point cloud completion methods tend to generate global shape skeletons and hence lack fine local details. Furthermore, they …

Point Cloud Completion

Variational Relational Point Completion Network for Robust 3D Classification

2023-04-18 · Liang Pan, Xinyi Chen, Zhongang Cai, Junzhe Zhang 외

Real-scanned point clouds are often incomplete due to viewpoint, occlusion, and noise, which hampers 3D geometric modeling and perception. Existing point cloud completion methods tend to generate global shape skeletons a…

3D Classification Classification Point Cloud Completion

POINTVIEW-GCN: 3D SHAPE CLASSIFICATION WITH MULTI-VIEW POINT CLOUDS

2021-09-22 · IEEE International Conference on Image Processing 2021 9 · Seyed Saber Mohammadi, Yiming Wang, Alessio Del Bue

We address 3D shape classification with partial point cloud inputs captured from multiple viewpoints around the object. Different from existing methods that perform classification on the complete point cloud by first re…

3D Point Cloud Classification 3D Shape Classification Classification Object