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A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence

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Abstract

In this paper, a novel compression framework based on 3D point cloud data is proposed for telepresence, which consists of two parts. One is implemented to remove the spatial redundancy, i.e., a robust Bayesian framework is designed to track the human motion and the 3D point cloud data of the human body is acquired by using the tracking 2D box. The other part is applied to remove the temporal redundancy of the 3D point cloud data. The temporal redundancy between point clouds is removed by using the motion vector, i.e., the most similar cluster in the previous frame is found for the cluster in the current frame by comparing the cluster feature and the cluster in the current frame is replaced by the motion vector for compressing the current frame. The first, the B-SHOT (binary signatures of histograms orientation) descriptor is applied to represent the point feature for matching the corresponding point between two frames. The second, the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current frame. The matching operation is exploited to find the corresponding clusters between the point cloud data of two frames. Finally, the cluster information in the current frame is replaced by the motion vector for compressing the current frame and the unsuccessfully matched clusters in the current and the motion vectors are transmitted into the remote end. In order to reduce calculation time of the B-SHOT descriptor, we introduce an octree structure into the B-SHOT descriptor. In particular, in order to improve the robustness of the matching operation, we design the cluster feature to estimate the similarity between two clusters. Experimental results have shown the better performance of the proposed method due to the lower calculation time and the higher compression ratio. The proposed method achieves the compression ratio of 8.42 and the delay time of 1 228 ms compared with the compression ratio of 5.99 and the delay time of 2 163 ms in the octree-based compression method under conditions of similar distortion rate.

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Acknowledgements

This work was supported by National Nature Science Foundation of China (No.61 811 530 281 and 61 861 136 009), Guangdong Regional Joint Foundation (No. 2019B1515120076), and the Fundamental Research for the Central Universities.

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Authors and Affiliations

  1. College of Automation Science and Engineering, South China University of Technology, Guangzhou, 510640, China

    Zun-Ran Wang, Chen-Guang Yang & Shi-Lu Dai

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  1. Zun-Ran Wang
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Correspondence to Chen-Guang Yang.

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Recommended by Associate Editor De Xu

Zun-Ran Wang received the B. Eng. degree in automation from the South China University of Technology, China in 2017. He is currently a M.Sc. degree candidate in the South China University of Technology, China.

His research interests include human-robot interaction, intelligent control and image processing.

Chen-Guang Yang received the B. Eng. degree in measurement and control from Northwestern Polytechnical University, China in 2005, the Ph.D. degree in control engineering from the National University of Singapore, Singapore in 2010. He received Best Paper Awards from IEEE Transactions on Robotics and over 10 international conferences. His research interests include robotics and automation.

Shi-Lu Dai received his B. Eng. degree in thermal engineering, the M. Eng. and Ph. D. degrees in control science and engineering, Northeastern University, China in 2002, 2006, and 2010, respectively. He was a visiting student in Department of Electrical and Computer Engineering, National University of Singapore, Singapore from November 2007 to November 2009, and a visiting scholar at Department of Electrical Engineering, University of Notre Dame, USA from October 2015 to October 2016. Since 2010, he has been with the School of Automation Science and Engineering, South China University of Technology, China, where he is currently a professor.

His research interests include adaptive and learning control, distributed cooperative systems.

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Wang, ZR., Yang, CG. & Dai, SL. A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence. Int. J. Autom. Comput. 17, 855–866 (2020). https://doi.org/10.1007/s11633-020-1240-5

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