  In this paper, we propose a new geometry coding method for point cloud
compression (PCC), where the points can be fitted and represented by straight
lines. 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.
