提出新方法压缩动态点云属性，显著降低冗余数据量。
Motion Estimation Techniques for Volumetric Video Attribute Compression
- 基于几何信息的属性帧间编码，提升压缩效率。
- 采用图结构运动估计，实现亚像素级精度匹配。
- 在多个基准上优于现有方法，适合高保真点云传输。
点云压缩依赖于对几何和属性的压缩技术。尽管基于运动的动态实体点云几何压缩已在几何基点云压缩（G-PCC）框架中取得显著率失真降低，但属性压缩的运动方法仍研究不足，难以有效消除属性的时间冗余。本文首先提出一种基于几何的帧间编码方案来压缩动态实体点云的属性；其次，提出一种基于图结构的点云属性运动估计方案；最后，提出一种无需插值的分数体素运动估计方法，将运动精度提升至分数体素级别。在MPEG点云数据集上的实验结果表明，所提方案在无损和有损几何条件下均优于G-PCC、GeS-TM和V-PCC。在有损几何条件下，平均比特率分别相比G-PCC、GeS-TM和V-PCC降低55.3%、42.3%和16.5%。
原文摘要 · Abstract (English)
Point cloud compression relies on techniques to compress both geometry and attributes. Motion-based approaches for dynamic solid point cloud geometry compression within the geometry-based point cloud compression (G-PCC) framework have achieved significant reductions in geometry rate. However, motion-based techniques for attribute compression remain underexplored, making it challenging to achieve significant reductions in the temporal redundancy of attributes. Firstly, this paper proposes a geometry-based inter-coding scheme to compress the attributes of dynamic solid point clouds. Secondly, a graph-based motion-estimation scheme for point-cloud attribute compression is proposed. Thirdly, an interpolation-free fractional-voxel motion estimation method is proposed to refine motion accuracy to fractional-voxel precision. Our experimental results on the MPEG point cloud dataset show that the proposed scheme outperforms G-PCC, GeS-TM, and V-PCC in lossless and lossy geometry conditions. We achieve average bitrate savings of $55.3\%$, $42.3\%$, and $16.5\%$ over G-PCC, GeS-TM, and V-PCC, respectively, under lossy-geometry conditions.
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