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

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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 1228 ms compared with the compression ratio of 5.99 and the delay time of 2163 ms in the octree-based compression method under conditions of similar distortion rate.

3D point cloud compressionmotion estimationsignatures of histograms orientation3D point cloud matchingpredicted frame and intra frame

Zun-Ran Wang、Chen-Guang Yang、Shi-Lu Dai

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

This work was supported by National Nature Science Foundation of ChinaThis work was supported by National Nature Science Foundation of ChinaGuangdong Regional Joint Foundationand the Fundamental Research for the Central Universities

61811530281618611360092019B1515120076

2020

10.1007/s11633-020-1240-5

机器智能研究(英文)
中国科学院自动化所

机器智能研究(英文)

CSTPCDCSCDEI
影响因子：0.49
ISSN：2731-538X
年,卷(期)：2020.17(6)
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