OpenPointCloud: An Open-Source Algorithm Library of Deep Learning Based Point Cloud Compression

被引:3
|
作者
Gao, Wei [1 ,2 ]
Ye, Hua [2 ]
Li, Ge [1 ]
Zheng, Huiming [1 ,2 ]
Wu, Yuyang [1 ,2 ]
Xie, Liang [1 ,2 ]
机构
[1] Peking Univ, Sch Elect & Comp Engn, Shenzhen Grad Sch, Shenzhen, Peoples R China
[2] Peng Cheng Lab, Inst Open Sources, Shenzhen, Peoples R China
基金
国家重点研发计划;
关键词
Point Cloud; Geometry Compression; Open-source Software; Algorithm Library; Deep Learning;
D O I
10.1145/3503161.3548545
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper gives an overview of OpenPointCloud, the first open-source algorithm library containing outstanding deep learning methods on point cloud compression (PCC). We provide an introduction of our implementations, including 8 methods on lossless geometry PCC and lossy geometry PCC. Principles and contributions of these methods in our algorithm library are illustrated, which are also implemented with different deep learning programming frameworks, such as TensorFlow, Pytorch and TensorLayer. In order to systematically evaluate the performances of all these methods, we conduct a comprehensive benchmarking test. We provide analyses and comparisons of their performances according to their categories and draw constructive conclusions. This algorithm library has been released at https://git.openi.org.cn/OpenPointCloud.
引用
收藏
页码:7347 / 7350
页数:4
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