Voxel-based Network for Shape Completion by Leveraging Edge Generation

Deep learning technique has yielded significant improvements in point cloud completion with the aim of completing missing object shapes from partial inputs. However, most existing methods fail to recover realistic structures due to over-smoothing of fine-grained details. In this paper, we develop a voxel-based network for point cloud completion by leveraging edge generation (VE-PCN). We first embed point clouds into regular voxel grids, and then generate complete objects with the help of the hallucinated shape edges. This decoupled architecture together with a multiscale grid feature learning is able to generate more realistic on-surface details. We evaluate our model on the Figure 1: We propose an edge-guiding and voxel-based publicly available completion datasets and show that it point cloud completion network to reconstruct complete outperforms existing state-of-the-art approaches quantitatively points from incomplete inputs.