Abstract
Abstract. Three-dimensional (3D) raster data (also named voxel) is important sources for 3D geo-information applications, which have long been used for modelling continuous phenomena such as geological and medical objects. Our world can be represented in voxels by gridding the 3D space and specifying what each grid represents by attaching every voxel to a real-world object. Nature-triggered disasters can also be modelled in volumetric representation. Unlike point cloud, it is still a lack of wide research on how to efficiently store and manage such semantic 3D raster data. In this work, we would like to investigate four different data layouts for voxel management in open-source (spatial) DBMS - PostgreSQL/PostGIS, which is suitable for efficiently retrieving and quick querying. Besides, a benchmark has been developed to compare various voxel data management solutions concerning functionality and performance. The main test dataset is the groups of buildings of UNSW Kensington Campus, with 10cm resolution. The obtained storage and query results suggest that the presented approach can be successfully used to handle voxel management, semantic and range queries on large voxel dataset.
Highlights
Voxels are volumetric pixels, which are spaced in a regular grid in three-dimensional (3D) space and are perceived without gaps between them
The regular voxel grid provides an ideal basis for deep Convolutional Neural Networks (CNNs), as has been recently demonstrated by Zhou and Tuzel (Zhou, Tuzel, 2018)
We investigate four data layouts for storing and managing the 3D raster data in PostgreSQL/PostGIS, namely - (1) a flat ARRAY table; (2) a POINT geometry table; (3) a MULTIPOINT geometry table, and (4) a PCPATCH table with the help of Pointcloud
Summary
Voxels are volumetric pixels, which are spaced in a regular grid in three-dimensional (3D) space and are perceived without gaps between them. The grid is regular and all possible voxel positions are spaced. Voxels are the quickest way to quickly model and visualize volumetric data (especially in natural or organic formations). Applications for voxels include the visualization and analysis of medical and scientific data (Chmielewski, Tompalski, 2017), and representation of terrain in games and simulations. Minecraft (Duncan, 2011), a sandbox video game, uses voxels to store terrain data. Scientists can use voxel-based modeling to visualize and measure the volume of anything from fluids to green spaces in urban centers (Anderson et al, 2018). The regular voxel grid provides an ideal basis for deep Convolutional Neural Networks (CNNs), as has been recently demonstrated by Zhou and Tuzel (Zhou, Tuzel, 2018)
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