Fine-Grained Patch Segmentation and Rasterization for 3-D Point Cloud Attribute Compression

Due to the high dimensionality of point cloud data and the irregularity and complexity of its geometric structure, effective attribute compression remains a very challenging task. Many recent efforts have focused on transforming point clouds into images and leveraging existing sophisticated image/video codecs to improve attribute coding efficiency. However, how to synthesize coherent and correlation-preserving attribute images is still inadequately addressed by existing studies, which are hindering the exertion of the merits of well-developed compression infrastructure. In this paper, we present a novel image synthesis method for effective point cloud attribute compression. Firstly, the proposed scheme segments a given point cloud into a collection of fine-grained patches by performing geometric structure analysis using heat kernel signature feature descriptor and complex points; Secondly, we transform the obtained patches from 3-D to 2-D using a low-dimensional embedding algorithm and then convert them into patch attribute images with the proposed patch rasterization and rectification method; And finally, we compactly assemble all the attribute images of patches together by formulating it as a bin nesting problem and harvest an attribute image of the whole point cloud for image/video-based compression. Experimental results demonstrate the effectiveness of the proposed method in point cloud attribute compression and its superiority over state-of-the-art codecs. The source code of this work is publicly available at https://github.com/pccompession/UPCAC.