A Transform Coding Strategy for Voxelized Dynamic Point Clouds

With the advent of virtual and augmented reality applications, 3D and free-viewpoint representations have evolved towards solid scene models using meshes and point clouds. Recent works have been addressing point clouds compression via octree-based hierarchical strategies in order to enable a multiresolution coding and visualization at a reasonable computational cost. This paper presents a voxelized dynamic point cloud coding scheme that combines a Cellular Automata block reversible transform for geometric data with a region adaptive transform for color data. Temporal redundancy is removed using a low-complexity prediction scheme to minimize the computational complexity and reduce the coded bit rate. Experimental results showed that the proposed solution obtained a significant bit rate reduction in lossless geometry coding and an improved rate-distortion performance in the lossy coding of color components with respect to state-of-the-art strategies.