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Voxelization algorithms for geospatial applications: Computational methods for voxelating spatial datasets of 3D city
Pirouz Nourian1, Romulo Gonçalves2, Sisi Zlatanova3
1TU Delft, Faculty of Architecture and the Built Environment, Department of Architectural Engineering + Technology, Design Informatics, Netherlands.
Methodsx
|July 14, 2016
Summary
This research introduces algorithms for creating accurate, large-scale voxel models from point clouds, curves, and surfaces. The developed methods ensure correct spatial and topological relations for environmental modeling applications.
Area of Science:
- Scientific Computation
- Environmental Modeling
- Computer Graphics
Background:
- Voxel representations are established in scientific computation and medical imaging.
- Existing methods may lack ease of use or spatial accuracy for large-scale environmental modeling.
- Accurate topological and semantic relations are crucial for built environment models.
Purpose of the Study:
- To present algorithms for generating voxel models from point cloud, curve, and surface data.
- To ensure spatial correctness, including topological and semantic relations.
- To provide easy access to methods for creating large-scale voxel models for environmental studies.
Main Methods:
- Developed algorithms for voxelizing point cloud, curve, and surface objects.
- Customized topological voxelization for surfaces and curves.
- Extended topological voxelization for point cloud data.
Main Results:
- Algorithms generate voxels (volumetric pixels) from various input data types.
- The software allows easy management of connectivity levels in voxels.
- The voxelization approach ensures spatial correctness and is independent of external libraries.
Conclusions:
- The presented algorithms facilitate the creation of spatially correct, large-scale voxel models.
- The software's independence from external libraries enhances integration.
- The methods are efficient and platform-independent, suitable for environmental modeling.
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