Self-supervised indoor scene point cloud completion from a single panorama

In this paper, we propose a self-supervised learning method of point cloud completion for indoor scenes. Considering the limited view of single-view image and the time-consuming and labor-intensive acquisition of multi-view images, we take panoramas as input, which makes the acquisition easier and the scope of the scene wider. As it is difficult to obtain complete scene point cloud, we design an auxiliary task to simulate scene missing area by shifting viewpoint of panorama and extract the supervision information of the scene itself. Given the difficulty to complete large-scale scene point cloud, we design a neighborhood integration and feature spreading module for feature extraction and reservation before substantial point cloud downsampling, enabling the completion network to handle large-scale point cloud. Then we propose a transformer-based scene point cloud completion network and show competitive completion results compared to relevant supervised learning methods.