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Point clouds obtained from 3D scans are typically sparse, irregular, andnoisy, and required to be consolidated. In this paper, we present the firstdeep learning based edge-aware technique to facilitate the consolidation ofpoint clouds. We design our network to process points grouped in local patches,and train it to learn and help consolidate points, deliberately for edges. Toachieve this, we formulate a regression component to simultaneously recover 3Dpoint coordinates and point-to-edge distances from upsampled features, and anedge-aware joint loss function to directly minimize distances from outputpoints to 3D meshes and to edges. Compared with previous neural network basedworks, our consolidation is edge-aware. During the synthesis, our network canattend to the detected sharp edges and enable more accurate 3D reconstructions.Also, we trained our network on virtual scanned point clouds, demonstrated theperformance of our method on both synthetic and real point clouds, presentedvarious surface reconstruction results, and showed how our method outperformsthe state-of-the-arts.

EC-Net: an Edge-aware Point set Consolidation Network