发表机构
Inria - Rennes Bretagne-Atlantique（法国国家信息与自动化研究所雷恩布列塔尼-大西洋分所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种整合进MPEG G-PCC标准的PPC属性压缩方法，经不同分辨率PPC测试，性能优于现有同类方案，有望成为新最优方案。
AI 中文摘要
全光点云（Plenoptic Point Clouds，PPC）是新型数据结构，可表示不同观测方向的光，为常规点云提供更高真实度，其实现方式是为每个点关联多种颜色而非单一颜色。本文提出一种高效压缩PPC属性的方法，该方法先对颜色属性进行Karhunen-Loève变换，再使用具备帧内预测能力的多个属性编码器。此压缩方案可整合进MPEG基于几何的点云压缩（Geometry-based PCC，G-PCC）标准，可采用G-PCC现有任意属性编码解决方案。针对不同空间分辨率PPC的压缩性能评估显示，与现有方法（如基于RAHT或视频的PCC解决方案）相比，该方案结果具竞争力，我们认为所提编码器将成为新的最优方案。
英文摘要
Plenoptic point clouds (PPC) are novel data structures that represent the light from different viewing directions in order to provide a higher degree of realism to regular point clouds. This is achieved by associating each point to multiple colors instead of a single one. Here, we present a method to efficiently compress the attributes of a PPC, consisting of a Karhunen-Loève transform over the color attributes followed by multiple attribute coders with intra prediction capability. This compression scheme can be incorporated within the MPEG's geometry-based PCC (G-PCC) standard, using any of G-PCC's existing solutions for attribute coding. Compression performance assessment using PPCs of different spatial resolutions reveals competitive results in comparison to existing methods, such as RAHT-based or video-based PCC solutions. We believe our coder to be the new state of the art.
DOI：10.1109/MMSP55362.2022.9949107
