PDP-NET: Patch-Based Dual-Path Network for Point Cloud Completion

Point cloud completion has become a popular research area in 3D computer vision. It aims to recover the complete point cloud from its partial observation. However, previous methods either directly predict the whole shape, change the original distribution of points, or have limited performance in reconstructing tiny and detailed object components. In this paper, we propose a novel Patch-based Dual-Path Network (PDP-Net) for point cloud completion, which leverages the advantages of different encoder architectures, with one path providing estimation for the global structure of the missing part, and the other path filling in the details by generating several point cloud patches. We also propose an identifier to retain the original points in the partial point cloud possibly. Comprehensive experiments and robustness tests demonstrate the effectiveness of our method even against different missing scales of the point cloud. Code available at: https://github.com/QifHE/PDP-Net-Public.