Compressed Geometric Arrays for Point Cloud Processing

TL;DR

This paper introduces a compressed geometric array format for point clouds that significantly improves processing speed and bandwidth efficiency by eliminating pointer-based indirections, outperforming traditional tree structures.

Contribution

The paper proposes a novel compressed geometric array format for point clouds that enhances processing efficiency and bandwidth utilization compared to existing tree-based methods.

Findings

Achieves 1328x speedup in point cloud operations.

Improves bandwidth utilization by 1321x.

Reduces data transfer volume by 54%.

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. 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).