We propose an intra frame predictive strategy for compression of 3D point cloud attributes. Our approach is integrated with the region adaptive graph Fourier transform (RAGFT), a multi-resolution transform formed by a composition of localized block transforms, which produces a set of low pass (approximation) and high pass (detail) coefficients at multiple resolutions. Since the transform operations are spatially localized, RAGFT coefficients at a given resolution may still be correlated. To exploit this phenomenon, we propose an intra-prediction strategy, in which decoded approximation coefficients are used to predict uncoded detail coefficients. The prediction residuals are then quantized and entropy coded. For the 8i dataset, we obtain gains up to 0.5db as compared to intra predicted point cloud compresion based on the region adaptive Haar transform (RAHT).

翻译：我们提出了压缩 3D 点云属性的框架内预测战略。 我们的方法与区域适应性图 Fourier 变异(RAGFT) 集成,即由局部区块变异构成的多分辨率变异,产生一套低传(接近)和高传(详细)系数的多分辨率变异,在多个分辨率上产生一套低传(接近)和高传(详细)系数。由于变异操作在空间上是局部的,某一分辨率上的RAGFT系数可能仍然相关。为了利用这一现象,我们提出了一种内置战略,其中使用解码近似系数来预测未编码的详细系数。然后,预测的剩余值被量化和加密编码。对于8i 数据集,我们从基于区域适应性海尔变(RAHT)的预测点云共和率中获得最多为0.5db的收益。