Towards Efficient Implementation of an Octree for a Large 3D Point Cloud.

Soohee Han

doi:10.3390/s18124398

Abstract

The present study introduces an efficient algorithm to construct a file-based octree for a large 3D point cloud. However, the algorithm was very slow compared with a memory-based approach, and got even worse when using a 3D point cloud scanned in longish objects like tunnels and corridors. The defects were addressed by implementing a semi-isometric octree group. The approach implements several semi-isometric octrees in a group, which tightly covers the 3D point cloud, though each octree along with its leaf node still maintains an isometric shape. The proposed approach was tested using three 3D point clouds captured in a long tunnel and a short tunnel by a terrestrial laser scanner, and in an urban area by an airborne laser scanner. The experimental results showed that the performance of the semi-isometric approach was not worse than a memory-based approach, and quite a lot better than a file-based one. Thus, it was proven that the proposed semi-isometric approach achieves a good balance between query performance and memory efficiency. In conclusion, if given enough main memory and using a moderately sized 3D point cloud, a memory-based approach is preferable. When the 3D point cloud is larger than the main memory, a file-based approach seems to be the inevitable choice, however, the semi-isometric approach is the better option.

Highlights

Advances in 3D terrestrial laser scanning technology and its various applications have increased the size of 3D point clouds enormously
A basic algorithm to construct an octree for a 3D point cloud is introduced
The query speed of a file-based approach is very poor and becomes even worse when dealing with very longish 3D point clouds scanned in tunnels and corridors

Summary

Introduction

Advances in 3D terrestrial laser scanning technology and its various applications have increased the size of 3D point clouds enormously. The methods can be categorized into two: lossy compression or abbreviation, and lossless indexing The former category eliminates less meaningful points from the 3D point cloud. Several relevant approaches have reported that the reduced data still exhibits consistent results with half or even less point density [1,2,3]. The latter category retains and uses the original coordinate information of all points, and uses special data structures to store and retrieve the data efficiently. K-d tree, which is a dynamic partitioning algorithm is more efficient and has been officially implemented in the point cloud library (PCL) [5]. Octree is being exploited by a number of Sensors 2018, 18, 4398; doi:10.3390/s18124398 www.mdpi.com/journal/sensors

Methods

Discussion

Conclusion