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

International Journal of Automation and computing 2020年第6期17卷 855-866页

作者： Zun-Ran Wang Chen-Guang Yang Shi-Lu Dai College of Automation Science and Engineering South China University of TechnologyGuangzhou 510640China

In this paper,a novel compression framework based on 3D point cloud data is proposed for telepresence,which consists of two *** 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 *** other part is applied to remove the temporal redundancy of the 3D point cloud *** 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 *** hrst,the B-SHOT(binary signatures of histograms orientation)descriptor is applied to represent the point feature for matching the corresponding point between two *** second,the K-mean algorithm is used to generate the cluster because there are a lot of unsuccessfully matched points in the current *** matching operation is exploited to find the corresponding clusters between the point cloud data of two ***,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 curren t and the motion vectors are transmit ted into the rem ote *** order to reduce calculation time of the B-SHOT descriptor,we introduce an octree structure into the B-SHOT *** particular,in order to improve the robustness of the matching operation,we design the cluster feature to estimate the similarity bet ween two *** tai results have shown the bet ter performance of the proposed method due to the lower calculation time and the higher compression *** proposed met hod 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

关键词： 3D point cloud compression motion estimation signatures of histograms orientation 3D point cloud matching predicted frame and intra frame.

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