This paper proposes a two-stage point cloud super resolution framework that combines local interpolation and deep neural network based readjustment. For the first stage, the authors apply a local interpolation method to increase the density and uniformity of the target point cloud. For the second stage, the authors employ an outer-product neural network to readjust the position of points that are inserted at the first stage. Comparison examples are given to demonstrate that the proposed framework achieves a better accuracy than existing state-of-art approaches, such as PU-Net, PointNet and DGCNN (Source code is available at https://github.com/qwerty1319/PC-SR).

本文提出了一个两阶段的点云超分辨率框架，该框架结合了局部插值和基于深度神经网络的重新调整。在第一阶段，作者应用局部插值方法来增加目标点云的密度和均匀性。对于第二阶段，作者使用外部产品神经网络来重新调整在第一阶段插入的点的位置。给出的比较示例表明，与现有的最先进方法（例如PU-Net，PointNet和DGCNN）相比，所提出的框架具有更高的准确性（源代码可从https://github.com/qwerty1319/PC-获得。 SR）。