论文标题
基于线性模型的几何编码激光雷达获得点云
Linear Model based Geometry Coding for Lidar Acquired Point Clouds
论文作者
论文摘要
在本文中，我们提出了一种用于点云压缩（PCC）的新几何编码方法，其中可以通过直线拟合和表示点。线性模型的编码可以用两个部分表示，包括沿线方向的原理组件和线的偏移。通过使用适当的量化步骤尺寸（QS）编码线性模型的参数来介绍紧凑的表示和高效率编码方法。为了最大程度地提高编码性能，采用编码器优化技术来找到编码位和错误之间的最佳权衡，涉及拉格朗日乘数方法，其中分析了QS和乘数方面的速率延伸行为。我们在MPEG G-PCC参考软件上实现了我们的方法，结果表明，所提出的方法在用明确的线结构（例如LIDAR获取的数据中获得自主驾驶数据）有效地编码点云。在有损耗的几何编码上，可以实现约20 \％的编码增长。
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.
