A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence
文献类型：期刊论文
| 作者 | Zun-Ran Wang; Chen-Guang Yang; Shi-Lu Dai |
| 刊名 | International Journal of Automation and Computing
 |
| 出版日期 | 2020 |
| 卷号 | 17期号:6页码:855-866 |
| 关键词 | 3D point cloud compression motion estimation signatures of histograms orientation 3D point cloud matching predicted frame and intra frame. |
| ISSN号 | 1476-8186 |
| DOI | 10.1007/s11633-020-1240-5 |
| 英文摘要 | 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 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/42385]  |
| 专题 | 自动化研究所_学术期刊_International Journal of Automation and Computing |
| 作者单位 | College of Automation Science and Engineering, South China University of Technology, Guangzhou 510640, China |
| 推荐引用方式 GB/T 7714 | Zun-Ran Wang,Chen-Guang Yang,Shi-Lu Dai. A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence[J]. International Journal of Automation and Computing,2020,17(6):855-866. |
| APA | Zun-Ran Wang,Chen-Guang Yang,&Shi-Lu Dai.(2020).A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence.International Journal of Automation and Computing,17(6),855-866. |
| MLA | Zun-Ran Wang,et al."A Fast Compression Framework Based on 3D Point Cloud Data for Telepresence".International Journal of Automation and Computing 17.6(2020):855-866. |
入库方式： OAI收割
来源：自动化研究所
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