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Title: Cylindrical Coordinates for Lidar Point Cloud Compression
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.
Authors:
; ;
Award ID(s):
1956190
Publication Date:
NSF-PAR ID:
10340129
Journal Name:
2021 IEEE International Conference on Image Processing (ICIP)
Page Range or eLocation-ID:
3083 to 3087
Sponsoring Org:
National Science Foundation
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