Coding Efficiency and Complexity Analysis of the Geometry-based Point Cloud Encoder

In recent years, 3D point cloud content has gained attention thanks to applications such as virtual, augmented or mixed reality, real-time immersive communications, and autonomous driving systems. However, raw point clouds comprise large amount of data, and compression is mandatory to allow efficient transmission and storage. The MPEG group proposed the standard called Geometry-based Point Cloud Compression (G-PCC) implemented in the Test Condition Category 1 and 3 (TMC13) software. TMC13 can significantly reduce the amount of data in static point clouds, but requires a high computational cost making real-time compression unfeasible, especially for devices with limited computational power and energy resources. This paper presents a computational cost and coding efficiency evaluation of the G-PCC coding tools. Two main contributions are presented: first, a coding efficiency analysis of different G-PCC tools; second, a complexity profiling of the G-PCC coding steps aimed at understanding the computational effort distribution. This work provides the first complexity assessment of G-PCC. The presented results can help in the proposal of complexity reduction approaches for the development of more efficient versions of G-PCC for real-time purposes.