Linear Model Based Geometry Coding for Lidar Acquired Point Clouds

In light of above observations, in this work, we propose a new model-based method for geometry coding in G-PCC. More specifically, it is a linear model that takes the explicit line structures as a prior knowledge to improve the coding efficiency. The encoding of the linear model can be expressed by two parts, including the principle component along the line direction and the offsets from the line. Compact representation and high-efficiency coding methods are presented by encoding the parameters of linear model with appropriate quantization step-sizes (QS). To maximize the coding performance, encoder optimization techniques are employed to find the optimal trade-off between coding bits and errors, involving the Lagrangian multiplier method, where the rate-distortion behavior in terms of QS and multiplier is analyzed. We implement our method on top of the MPEG G-PCC reference software, and the results have shown that the proposed method is effective in coding point clouds with explicit line structures, such as the Lidar acquired data for autonomous driving. About 20% coding gains can be achieved on lossy geometry coding.