Azimuth Adjustment Considering LiDAR Calibration for the Predictive Geometry Compression in G-PCC

Abstract

Point clouds captured by spinning Light Detection And Ranging (LiDAR) devices have played a significant role in many applications. To efficiently store and transmit such a huge amount of data, MPEG designed the Geometry-based Point Cloud Compression (G-PCC) standard, which includes a dedicated Pre-dictive Profile for LiDAR point clouds. In this scheme, Cartesian coordinates are mapped to the spherical representation using the elevation-related LiDAR calibration parameters to better characterize the spherical acquisition pattern of the spinning LiDAR device. As such, stronger spatial correlations exist among neighbouring nodes in the predictive structure, resulting in higher compression efficiency. However, it should be mentioned that the azimuth-related calibration parameters, which are unused, also impact the accuracy and correctness of the mapped spherical coordinates. In this paper, an azimuth adjustment method is proposed taking into account this impact. Experimental results show that the proposed azimuth adjustment can consistently improve the coding efficiency of G-PCC. Furthermore, a LiDAR calibration parameter estimation method is proposed in case the azimuth-related parameters are absent. Results show that the proposed calibration parameter estimation can precisely approximate the ground truth.