Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network
Two-Stage Point Cloud Super Resolution with Local Interpolation and Readjustment via Outer-Product Neural Network
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DOI：
10.1007/s11424-020-9266-x
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发表时间：
2020-09
影响因子：
2.1
通讯作者：
Guang-E Wang;Gang Xu;Qing Wu;Xundong Wu
中科院分区：
文献类型：
--
作者：
Guang-E Wang;Gang Xu;Qing Wu;Xundong Wu
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 ).
