Wang Jianqiang, Xue Ruixiang, Li Jiaxin, Ding Dandan, Lin Yi, Ma Zhan
IEEE Trans Pattern Anal Mach Intell. 2025 Jan;47(1):269-287. doi: 10.1109/TPAMI.2024.3462938. Epub 2024 Dec 4.
A universal multiscale conditional coding framework, Unicorn, is proposed to compress the geometry and attribute of any given point cloud. Geometry compression is addressed in Part I of this paper, while attribute compression is discussed in Part II. We construct the multiscale sparse tensors of each voxelized point cloud frame and properly leverage lower-scale priors in the current and (previously processed) temporal reference frames to improve the conditional probability approximation or content-aware predictive reconstruction of geometry occupancy in compression. Unicorn is a versatile, learning-based solution capable of compressing static and dynamic point clouds with diverse source characteristics in both lossy and lossless modes. Following the same evaluation criteria, Unicorn significantly outperforms standard-compliant approaches like MPEG G-PCC, V-PCC, and other learning-based solutions, yielding state-of-the-art compression efficiency while presenting affordable complexity for practical implementations.
提出了一种通用的多尺度条件编码框架Unicorn，用于压缩任何给定的点云的几何形状和属性。本文的第一部分讨论了几何压缩，而第二部分讨论了属性压缩。我们构建每个体素化点云帧的多尺度稀疏张量，并在当前帧和（先前处理的）时间参考帧中适当地利用较低尺度的先验信息，以改善压缩中几何占用的条件概率近似或内容感知预测重建。Unicorn是一种通用的、基于学习的解决方案，能够在有损和无损模式下压缩具有不同源特征的静态和动态点云。按照相同的评估标准，Unicorn显著优于符合标准的方法，如MPEG G-PCC、V-PCC以及其他基于学习的解决方案，在提供实用实现中可承受的复杂度的同时，产生了领先的压缩效率。
