The reconstruction of real-world surfaces is on high demand in various applications. Most existing reconstruction approaches apply 3D scanners for creating point clouds which are generally sparse and of low density. These points clouds will be triangulated and used for visualization in combination with surface normals estimated by geometrical approaches. However, the quality of the reconstruction depends on the density of the point cloud and the estimation of the surface normals. In this paper, we present a novel deep learning architecture for point cloud upsampling that enables subsequent stable and smooth surface reconstruction. A noisy point cloud of low density with corresponding point normals is used to estimate a point cloud with higher density and appendant point normals. To this end, we propose a compound loss function that encourages the network to estimate points that lie on a surface including normals accurately predicting the orientation of the surface. Our results show the benefit of estimating normals together with point positions. The resulting point cloud is smoother, more complete, and the final surface reconstruction is much closer to ground truth.

翻译：在各种应用中,实际世界表面的重建需求很大。大多数现有的重建方法都应用三维扫描仪来创造一般稀少和低密度的点云。这些点云将进行三角测绘,并与几何方法估计的表面正常值结合用于可视化。然而,重建的质量取决于点云的密度和表面正常值的估计。在本文件中,我们提出了一个用于点云取样的新的深层次学习结构,从而能够随后稳定和顺利地进行地面重建。使用低密度的低点云和相应的点常值来估计密度和附点正常值的点云。为此,我们提议了一个复合损失功能,鼓励网络估计表面的点,包括准确预测地表方向的正常值。我们的结果显示,与点位置一起估计正常值的好处。由此产生的点云更加平滑,更完整,最终的地表重建离地面真相要近得多。