Abstract. Point clouds obtained from 3D scans are typically sparse,
irregular, and noisy, and required to be consolidated. In this paper, we
present the first deep learning based edge-aware technique to facilitate
the consolidation of point clouds. We design our network to process
points grouped in local patches, and train it to learn and help consolidate
points, deliberately for edges. To achieve this, we formulate a regression
component to simultaneously recover 3D point coordinates and pointto-edge distances from upsampled features, and an edge-aware joint
loss function to directly minimize distances from output points to 3D
meshes and to edges. Compared with previous neural network based
works, our consolidation is edge-aware. During the synthesis, our network
can attend to the detected sharp edges and enable more accurate 3D
reconstructions. Also, we trained our network on virtual scanned point
clouds, demonstrated the performance of our method on both synthetic
and real point clouds, presented various surface reconstruction results,
and showed how our method outperforms the state-of-the-arts