HSG-Net: Point Cloud Completion via Heuristic Structure Growing.

Abstract

Existing point cloud completion methods rely on extracting latent codes from a partial point cloud to reconstruct a complete structure. However, the complexity of the partial point clouds, making the completion results of such methods less satisfactory, especially in long-distance (away from partial point cloud) areas. To tackle this challenge, we propose a point cloud completion network via heuristic structure growing (HSG-Net), which progressively completes the close-distance structure through an iterative heuristic structure growth strategy. Particularly, a novel data preprocessing (DP) method is proposed to obtain ground truth (GT) with specific structural integrity, guiding the network to learn close-distance structural information. In addition, the proposed consistency constraint displacement module (CCDM) is employed to fulfill structure growth, and a feature memory module (FMM) further enhances the quality of the grown structure. Furthermore, a proposed local information generator is used to further refine the structure-grown point cloud, fetching the final result. Extensive quantitative and qualitative results demonstrate that our HSG-Net outperforms the state-of-the-art methods.