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  • 出版日期:2021-02-25 发布日期:2021-02-02

. [J]. 系统科学与复杂性, 2021, 34(1): 68-82.

WANG Guangyu · XU Gang · WU Qing · WU Xundong. Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network[J]. Journal of Systems Science and Complexity, 2021, 34(1): 68-82.

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

WANG Guangyu · XU Gang · WU Qing · WU Xundong   

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018, China; Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou 310018, China. Email: wangyu19950617@gmail.com; gxu@hdu.edu.cn; wuq@hdu.edu.cn; wuxd@hdu.edu.cn.
  • Online:2021-02-25 Published:2021-02-02

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).

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