  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. Tree based 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 1328x speed up, 1321x 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).
