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

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.

A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

“…So, it is easy to implement. Paper [7] adopts a hash-like structure for storing a multi-dimensional spatial data, and it has the potential to process Point Cloud data. Octree is a 3-dimension extension of Quad-tree [8] or an adaptive Grid structure, and is widely recognized as a promising representation of Point Cloud [9].…”

Airborne LiDAR-Derived Digital Elevation Model for Archaeology

“…Thus, this data structure is less and less suitable to represent the data from modern data acquisition sources, such as full waveform airborne LiDAR data. Therefore, methods to voxelize point cloud data into 3D polygon models are being actively explored, e.g., [32]. For archaeological analysis, however, the usability of the 3D model currently lags far behind DEMs due to the availability of suitable software tools and processing pipelines.…”