Cylindrical Coordinates for Lidar Point Cloud Compression
Cylindrical Coordinates for Lidar Point Cloud Compression
复制标题
DOI：
10.1109/icip42928.2021.9506448
复制
发表时间：
2021-06
期刊：
影响因子：
--
通讯作者：
Shashank N. Sridhara;Eduardo Pavez;Antonio Ortega
中科院分区：
文献类型：
--
作者：
Shashank N. Sridhara;Eduardo Pavez;Antonio Ortega
我们提出了一种有效的体素化方法来编码的几何形状和属性的3D点云从自动驾驶汽车。由于传感器的圆形扫描轨迹，LiDAR点云的几何形状与RGBD相机捕获的点云的几何形状本质上不同。我们的方法利用这些特定的属性来表示圆柱坐标中的点，而不是传统的笛卡尔坐标。我们证明，区域自适应分层变换（RAHT）可以扩展到这个设置，导致属性编码的基础上，在圆柱坐标的体积分区。实验结果表明，我们提出的体素化优于传统的方法，基于笛卡尔坐标的这种类型的数据。我们观察到一个显着的改善，属性编码的性能，减少5-10%的比特率和八叉树表示减少35-45%的位。
We present an efficient voxelization method to encode the geometry and attributes of 3D point clouds obtained from autonomous vehicles. Due to the circular scanning trajectory of sensors, the geometry of LiDAR point clouds is inherently different from that of point clouds captured from RGBD cameras. Our method exploits these specific properties to representing points in cylindrical coordinates instead of conventional Cartesian coordinates. We demonstrate that Region Adaptive Hierarchical Transform (RAHT) can be extended to this setting, leading to attribute encoding based on a volumetric partition in cylindrical coordinates. Experimental results show that our proposed voxelization outperforms conventional approaches based on Cartesian coordinates for this type of data. We observe a significant improvement in attribute coding performance with 5-10% reduction in bitrate and octree representation with 35-45% reduction in bits.