A Comparative Study about Data Structures Used for Efficient Management of Voxelised Full-Waveform Airborne LiDAR Data during 3D Polygonal Model Creation

Milto Miltiadou,Michael G Grant,Neill D F Campbell,Darren Cosker

doi:10.3390/rs13040559

Milto Miltiadou, Michael G Grant + Show 2 more

Open Access

https://doi.org/10.3390/rs13040559

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Abstract

In this paper, we investigate the performance of six data structures for managing voxelised full-waveform airborne LiDAR data during 3D polygonal model creation. While full-waveform LiDAR data has been available for over a decade, extraction of peak points is the most widely used approach of interpreting them. The increased information stored within the waveform data makes interpretation and handling difficult. It is, therefore, important to research which data structures are more appropriate for storing and interpreting the data. In this paper, we investigate the performance of six data structures while voxelising and interpreting full-waveform LiDAR data for 3D polygonal model creation. The data structures are tested in terms of time efficiency and memory consumption during run-time and are the following: (1) 1D-Array that guarantees coherent memory allocation, (2) Voxel Hashing, which uses a hash table for storing the intensity values (3) Octree (4) Integral Volumes that allows finding the sum of any cuboid area in constant time, (5) Octree Max/Min, which is an upgraded octree and (6) Integral Octree, which is proposed here and it is an attempt to combine the benefits of octrees and Integral Volumes. In this paper, it is shown that Integral Volumes is the more time efficient data structure but it requires the most memory allocation. Furthermore, 1D-Array and Integral Volumes require the allocation of coherent space in memory including the empty voxels, while Voxel Hashing and the octree related data structures do not require to allocate memory for empty voxels. These data structures, therefore, and as shown in the test conducted, allocate less memory. To sum up, there is a need to investigate how the LiDAR data are stored in memory. Each tested data structure has different benefits and downsides; therefore, each application should be examined individually.

Highlights

IntroductionThere are used to be two types of LiDAR data: discrete and the full-waveform (FW) [1]
It worth highlighting that this paper investigates the performance of six data structures perform on iso-surface extraction (3D polygonal model creation) of voxelised full-waveform
The smaller the voxel length is, the more voxels exist because when the voxel length decreases the resolution of the voxelised FW LiDAR data increases

Summary

Introduction

There are used to be two types of LiDAR data: discrete and the full-waveform (FW) [1]. Each data sample has its own associated unique key value. By using a “hash function”, the unique key value is translated into the index of the data sample, and the data sample can be accessed very fast—at constant time O(n)—in memory. In the “Voxel Hashing” approach, the intensities of the voxel values are stored inside a hashed table and their unique keys are relevant to the positions of the voxels inside the volume. To [33], this approach reduces overheads in traversal time of hierarchical structures (process of visiting a node in a tree structure) and on top of that it reduces memory allocation because empty voxels are not stored

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