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The reconstruction of real-world surfaces is on high demand in variousapplications. Most existing reconstruction approaches apply 3D scanners forcreating point clouds which are generally sparse and of low density. Thesepoints clouds will be triangulated and used for visualization in combinationwith surface normals estimated by geometrical approaches. However, the qualityof the reconstruction depends on the density of the point cloud and theestimation of the surface normals. In this paper, we present a novel deeplearning architecture for point cloud upsampling that enables subsequent stableand smooth surface reconstruction. A noisy point cloud of low density withcorresponding point normals is used to estimate a point cloud with higherdensity and appendant point normals. To this end, we propose a compound lossfunction that encourages the network to estimate points that lie on a surfaceincluding normals accurately predicting the orientation of the surface. Ourresults show the benefit of estimating normals together with point positions.The resulting point cloud is smoother, more complete, and the final surfacereconstruction is much closer to ground truth.

Point Cloud Upsampling and Normal Estimation using Deep Learning for Robust Surface Reconstruction