A novel method for point cloud completion: Adaptive region shape fusion network

Abstract

Point cloud data obtained by 3D scanning equipment is often incomplete. In recent years, to complete the missing point clouds has become an important task in computer visualization research. This paper adopts a coarse-to-fine completion strategy and build a novel adaptive region shape fused network (ARSF-Net), which contains three core modules, namely region shape encoding module (RSE), adaptive feature selection-aggregation module (ASA), and encoding-attention transformer module (EAT). The RSE module adaptively aggregates the latent shape information contained in local features according to the feature strength. In ASA module, we first treat point coordinates and shape features as parent nodes and design a hybrid correlation method to adaptively group parent nodes. Then, each set of parent nodes generates a child node. Finally, we splice the features and points in the parent node and child node separately to double the number. For EAT module, we learn features from the encoding stage and use a coordinate-based embedding transformer to generate uniform high-resolution point clouds. Compared with previous methods, we pay special attention to the difference among the latent shape information contained in the local point clouds, thus making the local feature extraction more interpretable. At the same time, to generate valid detail features from the original ones, we abundantly consider the correlation among the original ones, and directly combine the original features with the generated ones. Our experiments on different datasets verify the good performance of ARSF-Net in the point cloud completion task.