.png){kind=icon}
返回
3D Point Cloud Attribute Compression via Graph Prediction
.png){kind=icon}
PRE
AI
DOI：10.1109/LSP.2019.2963793.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
3D point clouds associated with attributes are considered as a promising data representation for immersive communication. The large amount of data, however, poses great challenges to the subsequent transmission and storage processes. In this letter, we propose a new compression scheme for the color attribute of static voxelized 3D point clouds. Specifically, we first partition the colors of a 3D point cloud into clusters by applying k-d tree to the geometry information, which are then successively encoded. To eliminate the redundancy, we propose a novel prediction module, namely graph prediction, in which a small number of representative points selected from previously encoded clusters are used to predict the points to be encoded by exploring the underlying graph structure constructed from the geometry information. Furthermore, the prediction residuals are transformed with the graph transform, and the resulting transform coefficients are finally uniformly quantified and entropy encoded. Experimental results show that the proposed compression scheme is able to achieve better rate-distortion performance at a lower computational cost when compared with state-of-the-art methods.
Keyword:
3D point cloud
compression
prediction
graph structure
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
机构
引用论文
Clinical and chest computed tomography features of patients suffering from mild and severe COVID-19 at Fayoum University Hospital in Egypt.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏
分享收藏
查看更多内容