Compressed Geometric Arrays for Point Cloud Processing

Abstract

The ever-increasing demand for 3D modeling in the emerging immersive applications has made point clouds an essential class of data for 3D image and video processing. Treebased structures are commonly used for representing point clouds where pointers are used to realize the connection between nodes. Tree-based structures significantly suffer from irregular access patterns for large point clouds. Memory access indirection in such structures is disruptive to bandwidth efficiency and performance. In this paper, we propose a point cloud representation format based on compressed geometric arrays (CGA). Then, we examine new methods for point cloud processing based on CGA. The proposed format enables a higher bandwidth efficiency via eliminating memory access indirections (i.e., pointer chasing at the nodes of tree) thereby improving the efficiency of point cloud processing. Our experimental results show that using CGA for point cloud operations achieves 1328× speed up, 1321× better bandwidth utilization, and 54% reduction in the volume of transferred data as compared to the state-of-the-art tree-based format from point cloud library (PCL). Index Terms-point cloud representations point cloud operations spatial/temporal coding memory managementHoda Roodaki is with K.

Automatic Roadside Feature Detection Based on Lidar Road Cross Section Images

“…A point cloud is a set of geometric points with coordinates sampled from 3D space [ 33 ]. Point clouds are usually generated by computer graphics or acquired by Lidar to represent 3D objects.…”