论文标题
3D点云的区域自适应图傅立叶变换
Region adaptive graph fourier transform for 3d point clouds
论文作者
论文摘要
我们介绍了区域自适应图傅立叶变换（RA-GFT），以压缩3D点云属性。 RA-GFT是一种多分辨率变换，是通过将空间局部块变换结合在一起而形成的。我们假设这些点是由一个由根树代表的嵌套隔板家族组织的。在每个分辨率级别，使用块变换在簇中处理属性。每个块变换都会产生单个近似值（DC）系数和各种细节（AC）系数。将直流系数提升到下一个（较低分辨率）水平，在该水平上可以重复该过程直到达到根。由于簇可能具有不同的点，因此每个块变换必须结合每个系数的相对重要性。为此，我们介绍了$ \ mathbf {q} $ - 归一化图Laplacian，并建议将其特征向量作为块变换。与以前的方法相比，RA-GFT实现更好的复杂性 - 绩效权衡。特别是，它的表现优于区域自适应HAAR变换（RAHT）高达2.5 dB，头顶上的复杂性很小。
We introduce the Region Adaptive Graph Fourier Transform (RA-GFT) for compression of 3D point cloud attributes. The RA-GFT is a multiresolution transform, formed by combining spatially localized block transforms. We assume the points are organized by a family of nested partitions represented by a rooted tree. At each resolution level, attributes are processed in clusters using block transforms. Each block transform produces a single approximation (DC) coefficient, and various detail (AC) coefficients. The DC coefficients are promoted up the tree to the next (lower resolution) level, where the process can be repeated until reaching the root. Since clusters may have a different numbers of points, each block transform must incorporate the relative importance of each coefficient. For this, we introduce the $\mathbf{Q}$-normalized graph Laplacian, and propose using its eigenvectors as the block transform. The RA-GFT achieves better complexity-performance trade-offs than previous approaches. In particular, it outperforms the Region Adaptive Haar Transform (RAHT) by up to 2.5 dB, with a small complexity overhead.
