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

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Abstract

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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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, 310018, China

    Guangyu Wang, Gang Xu, Qing Wu & Xundong Wu

  2. Key Laboratory of Complex Systems Modeling and Simulation, Ministry of Education, Hangzhou, 310018, China

    Guangyu Wang, Gang Xu, Qing Wu & Xundong Wu

Authors
  1. Guangyu Wang
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  2. Gang Xu
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  3. Qing Wu
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  4. Xundong Wu
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Corresponding author

Correspondence to Xundong Wu.

Additional information

This research was supported by the NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization under Grant No. U1909210, and the National Nature Science Foundation of China under Grant Nos. 61761136010, 61772163.

This paper was recommended for publication by Editor-in-Chief GAO Xiao-Shan.

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Wang, G., Xu, G., Wu, Q. et al. Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network. J Syst Sci Complex 34, 68–82 (2021). https://doi.org/10.1007/s11424-020-9266-x

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  • DOI: https://doi.org/10.1007/s11424-020-9266-x

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