论文标题
点云属性通过连续的子空间图转换
Point Cloud Attribute Compression via Successive Subspace Graph Transform
论文作者
论文摘要
受最近提出的连续子空间学习（SSL）原则的启发，我们开发了连续的子空间图转换（SSGT），以解决这项工作中的点云属性压缩。 OCTREE几何结构用于划分点云，其中OCTREE的每个节点代表具有一定空间大小的点云子空间。我们设计了一个使用自动循环的加权图来描述子空间并根据标准化图拉普拉斯（Laplacian）定义图形傅立叶变换。将转换应用于从叶子节点到OCTREE的根节点的大点云，而代表的子空间则从最小的子空间连续扩展到整个点云。通过实验结果表明，提出的SSGT方法比以前的区域自适应HAAR变换（RAHT）方法提供了更好的R-D性能。
Inspired by the recently proposed successive subspace learning (SSL) principles, we develop a successive subspace graph transform (SSGT) to address point cloud attribute compression in this work. The octree geometry structure is utilized to partition the point cloud, where every node of the octree represents a point cloud subspace with a certain spatial size. We design a weighted graph with self-loop to describe the subspace and define a graph Fourier transform based on the normalized graph Laplacian. The transforms are applied to large point clouds from the leaf nodes to the root node of the octree recursively, while the represented subspace is expanded from the smallest one to the whole point cloud successively. It is shown by experimental results that the proposed SSGT method offers better R-D performances than the previous Region Adaptive Haar Transform (RAHT) method.
