Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product N

• 作 者: WANG Guangyu XU Gang WU Qing WU Xundong
• 机 构: School of Computer Science and Technology,Hangzhou Dianzi University,Hangzhou 310018,China;Key Labor
• 出 处: 系统科学与复杂性学报（英文版）
• 发表日期: 2021年1期
• 关键词: Neural network outer-product network point cloud super resolution

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).