GANet: Graph-Aware Network for Point Cloud Completion with Displacement-Aware Point Augmentor

Hongyu Yan, Haoxi Ran, Li Lu

Keywords: Point cloud completion, Graph-aware network, Displacements-aware point augmentor

Abstract: Remarkably, real-world data (e.g., LiDAR-based point clouds) is commonly sparse, uneven, occluded, and truncated. The point cloud completion task draws due attention, which aims to predict a complete and accurate shape from its partial observation. However, existing methods commonly adopt PointNet or PointNet++ to extract features of incomplete point clouds. In this paper, we propose an end-to-end Graph-Aware Network (\textbf{GANet}) to effectively learn from the contour information of the partial point clouds. Moreover, we design Displacements-Aware Augmentor (DPA) to upsample and refine coarse point clouds. With our graph-based feature extractors and Displacements-Aware Transformer, our DPA can precisely capture the geometric and structural features to refine the complete point clouds. Experiments on PCN and MVP datasets demonstrate that our GANet achieves state-of-the-art on the task of shape completion.

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TL;DR: Our proposed GANet effectively learns from contour information of partial point clouds, and it delivers state-of-the-art results on multiple benchmarks and exhibits impressive efficiency.