  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
thatRegion 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.
