SD-Net: Spatially-Disentangled Point Cloud Completion Network

Abstract

Point clouds obtained from 3D scanning are typically incomplete, noisy, and sparse. Previous completion methods aim to generate complete point clouds, while taking into account the densification of point clouds, filling small holes, and proximity-to-surface, all through a single network. After revisiting the task, we propose SDNet, which disentangles the task based on the spatial characteristics of point clouds and formulates two sub-networks, a Dense Refiner and a Missing Generator. Given a partial input, the Dense Refiner produces a dense and clean point cloud, as a more reliable partial surface, which assists the Missing Generator to better infer the remaining point cloud structure. To promote the alignment and interaction across these two modules, we propose a Cross Fusion Unit with designed Non-Symmetrical Cross Transformers to capture geometric relationships between partial and missing regions, contributing to a complete, dense and well-aligned output. Extensive quantitative and qualitative results demonstrate that our method outperforms the state-of-the-art methods.