A Hybrid Compression Framework for Color Attributes of Static 3D Point Clouds

The emergence of 3D point clouds (3DPCs) is promoting the rapid development of immersive communication, autonomous driving, and so on. Due to the huge data volume, the compression of 3DPCs is becoming more and more attractive. We propose a novel and efficient color attribute compression method for static 3DPCs. First, a 3DPC is partitioned into several sub-point clouds by color distribution analysis. Each sub-point cloud is then decomposed into a lot of 3D blocks by an improved k-d tree-based decomposition algorithm. Afterwards, a novel virtual adaptive sampling-based sparse representation strategy is proposed for each 3D block to remove the redundancy among points, in which the bases of the graph transform (GT) and the discrete cosine transform (DCT) are used as candidates of the complete dictionary. Experimental results over 10 common 3DPCs demonstrate that the proposed method can achieve superior or comparable coding performance when compared with the current state-of-the-art methods.

Automated summary

This study proposes a novel and efficient color attribute compression method for static 3D point clouds, which involves partitioning the point cloud based on color distribution analysis, decomposing them into 3D blocks using an improved k-d tree algorithm, and utilizing a sparse representation strategy with virtual adaptive sampling. Experimental results show that this method achieves superior coding performance compared to current state-of-the-art methods.