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 partialto-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 virtualscanned 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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Pythagorean fuzzy set (PFS) is applied to the problems of multi-attribute decision-making, and a similarity measure of grey relational analysis (GRA) and PFS based novel method for MADM is presented, and the corresponding algorithm is designed. Firstly, a new similarity measurement method is proposed, and some important properties are proved. Furthermore, the ranking of decision options is achieved by comparing the grey relational similarity of each option with the optimal and worst ideal sequences of the PFS when the attribute values are considered as the Pythagorean fuzzy number (PFN). Finally, a detailed example and comparative experiments are illustrated to prove the effectiveness and correctness of the proposed method.