PS-Net: Point Shift Network for 3-D Point Cloud Completion
Yirui Zhang, Jiabo Xu, Yanni Zou, Peter Liu, Jie Liu
Abstract
Point cloud completion aims to infer the complete point clouds from incomplete ones, which is used in remote sensing applications such as reconstructing and autonomous driving. However, most existing methods cannot recover accurate structure details of the object. In this paper, we propose point shift network (PS-Net). Our main contributions lie in the following three-folds. First, we propose a multi-resolution encoder, which extracts and fuses multi-resolution point cloud features hierarchically, thus avoiding information loss caused by a single global feature. Second, we design a multi-resolution point cloud generation structure, which can be combined with the multi-resolution encoder to generate gradually dense point clouds, avoiding the problem of non-uniformly density of the single-layer decoder. Third, we design the shift network, which is used to generate shift vectors to shift the coordinates of each point cloud, so as to further finetune the coordinate positions of point clouds, achieving more accurate prediction. We conduct comprehensive experiments on ShapeNet, KITTI, ScanObjectNN, and ModelNet40 datasets, which demonstrate that the proposed PS-Net achieves better performance than existing methods and verify the robustness of the proposed method. This paper contributes a new method to point cloud completion, realizes fine point cloud shape completion, and brings new possibilities to the research of autonomous driving, registration, and reconstruction.