Learning Point Cloud Completion without Complete Point Clouds: A Pose-Aware Approach

Abstract

Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of incomplete and complete point clouds, which are labor-intensive to obtain. Unpaired methods have been proposed, but still require a set of complete point clouds as a reference. As a remedy, in this paper, we propose a novel point cloud completion framework without using any complete point cloud at all. Our main idea is to generate multiple incomplete point clouds of various poses and integrate them into a complete point cloud. We train our framework based on cycle consistency, to generate an incomplete point cloud such that 1) shares the same object as the input incomplete point cloud and 2) corresponds to an arbitrarily given pose. In addition, we devise a novel projection method conditioned by pose to gather visible features, from a volumetric feature extracted by an encoder. Extensive experiments demonstrate that the proposed method achieves comparable or better results than existing unpaired methods. Further, we show that our method also can be applied to real incomplete point clouds.