Attribute-aware Partitioning for Graph-based Point Cloud Attribute Coding

The unstructured nature of point cloud data makes compression of their attributes very challenging. In this paper, the known approach of using the Graph Fourier Transform on partitions of the point cloud is improved. It is proposed to make the partitioning process both geometry and attribute-aware, taking all of the point cloud’s characteristics into account simultaneously. Additional information, that allows the decoder to reproduce the partitioning of the encoder, is added to the bitstream. Furthermore, a refinement algorithm which re-estimates the partitioning information at the encoder with the decoder in mind is proposed. Experiments show that the baseline method is outperformed in Bjøntegaard Delta rate reduction by 2.39%, reaching as much as 3.58% at high bitrates.