U-Motion通过分层运动建模实现点云视频高效压缩。
U-Motion: Learned Point Cloud Video Compression with U-Structured Temporal Context Generation
- 采用分层时序结构，分尺度进行运动估计与补偿。
- 在密集动态点云测试中，几何与属性压缩性能优于现有方法。
- 适合需要高保真3D动态场景压缩的工业应用。
点云视频（PCV）是动态场景的多功能3D表示，应用前景广阔。本文提出U-Motion，一种基于学习的点云视频几何与属性压缩方案。设计了分层帧间预测框架U-Inter，通过自顶向下（细粒度到粗粒度）运动传播、自底向上运动预测编码以及多尺度组运动补偿，在不同尺度上实现精确运动估计与高效运动压缩。此外，还引入多尺度时空预测编码模块，捕捉U-Inter预测后仍存在的跨尺度空间冗余。实验遵循MPEG通用测试条件，针对密集动态点云进行评估，结果表明U-Motion在几何与属性压缩方面均显著优于MPEG G-PCC-GesTM v3.0及近期学习型方法。
原文摘要 · Abstract (English)
Point cloud video (PCV) is a versatile 3D representation of dynamic scenes with emerging applications. This paper introduces U-Motion, a learning-based compression scheme for both PCV geometry and attributes. We propose a U-Structured inter-frame prediction framework, U-Inter, which performs explicit motion estimation and compensation (ME/MC) at different scales with varying levels of detail. It integrates Top-Down (Fine-to-Coarse) Motion Propagation, Bottom-Up Motion Predictive Coding and Multi-scale Group Motion Compensation to enable accurate motion estimation and efficient motion compression at each scale. In addition, we design a multi-scale spatial-temporal predictive coding module to capture the cross-scale spatial redundancy remaining after U-Inter prediction. We conduct experiments following the MPEG Common Test Condition for dense dynamic point clouds and demonstrate that U-Motion can achieve significant gains over MPEG G-PCC-GesTM v3.0 and recently published learning-based methods for both geometry and attribute compression.
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