Point Cloud Attribute Compression via Successive Subspace Graph Transform
Point Cloud Attribute Compression via Successive Subspace Graph Transform
复制标题
通过连续子空间图变换进行点云属性压缩
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
C. J. Kuo
中科院分区：
文献类型：
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作者：
Yueru Chen;Yiting Shao;Jing Wang;Ge Li;C. J. Kuo
受最近提出的连续子空间学习（SSL）原理的启发，我们开发了连续子空间图变换（SSGT）来解决这项工作中的点云属性压缩问题。利用八叉树几何结构对点云进行划分，八叉树的每个节点代表一个具有一定空间尺寸的点云子空间。我们设计了一个具有自循环的加权图来描述子空间，并基于归一化图拉普拉斯定义了图傅立叶变换。变换从八叉树的叶节点到根节点递归地应用于大点云，而表示的子空间依次从最小的子空间扩展到整个点云。实验结果表明，所提出的 SSGT 方法比之前的区域自适应 Haar 变换（RAHT）方法具有更好的 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.