用K-L变换与预测编码压缩光场点云属性，提升渲染真实感。
Geometry-Based Compression of Plenoptic Point Clouds
- 对颜色属性做K-L变换降维，再用带内预测的编码器压缩
- 在不同分辨率下压缩率优于现有视频或RAHT方法
- 适合需高保真渲染的3D点云应用，如虚拟现实
光场点云（PPC）是一种新型数据结构，通过为每个点关联多个颜色而非单一颜色，从不同视角呈现光线信息，从而提升普通点云的真实感。本文提出一种高效压缩PPC属性的方法，包括对颜色属性进行Karhunen-Loève变换，并结合具备内预测能力的多路属性编码器。该方案可无缝集成至MPEG的基于几何的点云编码（G-PCC）标准中，兼容其现有的属性编码方案。在不同空间分辨率的PPC数据上进行压缩性能评估，结果表明其表现优于现有方法，如基于RAHT或视频的点云编码方案。我们认为该编码器已达到当前最优水平。
原文摘要 · Abstract (English)
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.
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