Rate-Distortion Driven Adaptive Partitioning for Octree-Based Point Cloud Geometry Coding

Point clouds are widely recognized as a promising 3D visual representation model for future 3D applications, e.g. Augmented/Virtual Reality and autonomous mobile navigation. To enable immersive and high quality experiences, a large amount of data needs to be transmitted, and thus, efficient compression techniques are a key factor for its success. Nowadays, many point cloud coding techniques rely on layered octree data structures able to provide multiple benefits, notably level of detail scalability. However, the octree creation process still lacks flexibility compared to the optimal partitioning methods popular in 2D encoders, which are mainly driven by rate-distortion trade-offs. The goal of this paper is to propose a novel octree creation mechanism which is able to exploit the intrinsic point cloud resolution by iteratively partitioning the octree voxels by means of split decisions based on a Rate-Distortion Optimization process. In addition, the arithmetic coding step is adapted to the density at each octree depth. This novel approach allows reaching a significant bitrate reduction, notably 5% in average, and up to 9.8%, over the adopted coding anchor using state-of-the-art octree based coding.