.png){kind=icon}
返回
Point cloud coding: A privileged view driven by a classification taxonomy
.png){kind=icon}
PRE
AI
DOI：10.1016/j.image.2020.115862.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Humans mainly communicate among them and with the world around them using light and vision, thus implying that visual representation technologies play a central role in human societies. While visual representation has been based on the 2D representation paradigm for many decades, multiple developments are nowadays pressing towards the adoption of more realistic and immersive 3D visual representation models. Point clouds are one of these emerging representation models. However, the huge amount of data involved asks for highly efficient coding solutions, some of which have recently started to be developed by the MPEG and JPEG standardization groups. In this hectic context, this paper proposes a privileged view over the current point cloud coding technologies, driven by a novel, appropriate classification taxonomy. For this purpose, some of the most representative point cloud coding solutions available in the literature will be reviewed to exercise the most relevant classification paths in the proposed taxonomy. It is expected that this type of classification taxonomy and privileged view may help better understanding the point cloud coding landscape for further solid and consistent advancements in this emerging technical area.
Keyword:
Point cloud coding
Taxonomy
Geometry
Attributes
Voxel
Octree
Patch
Graph
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
S
IF:
2.7
论文数:
2.8K
被引数:
4.2K
机构
引用论文
.png){kind=icon}
Compression of 3-D point visual data using vector quantization and rate-distortion optimization
PREAI
分享收藏
查看更多内容