  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 point-to-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.
