Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network
When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utiliz...
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MDPI AG
2025-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/4/615 |
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| author | Martin Štroner Martin Boušek Jakub Kučera Hana Váchová Rudolf Urban |
| author_facet | Martin Štroner Martin Boušek Jakub Kučera Hana Váchová Rudolf Urban |
| author_sort | Martin Štroner |
| collection | DOAJ |
| description | When filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a “voxel cube” of 9 × 9 × 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work. |
| format | Article |
| id | doaj-art-a2fed06c71414d67a57bee705eb8ee09 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-a2fed06c71414d67a57bee705eb8ee092025-08-20T03:12:09ZengMDPI AGRemote Sensing2072-42922025-02-0117461510.3390/rs17040615Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural NetworkMartin Štroner0Martin Boušek1Jakub Kučera2Hana Váchová3Rudolf Urban4Department of Special Geodesy, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 29 Prague, Czech RepublicDepartment of Special Geodesy, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 29 Prague, Czech RepublicDepartment of Special Geodesy, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 29 Prague, Czech RepublicDepartment of Special Geodesy, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 29 Prague, Czech RepublicDepartment of Special Geodesy, Faculty of Civil Engineering, Czech Technical University in Prague, Thákurova 7, 166 29 Prague, Czech RepublicWhen filtering highly rugged terrain from dense point clouds (particularly in technical applications such as civil engineering), the most widely used filtering approaches yield suboptimal results. Here, we proposed and tested a novel ground-filtering algorithm, a multi-size voxel cube (MSVC), utilizing a deep neural network. This is based on the voxelization of the point cloud, the classification of individual voxels as ground or non-ground using surrounding voxels (a “voxel cube” of 9 × 9 × 9 voxels), and the gradual reduction in voxel size, allowing the acquisition of custom-level detail and highly rugged terrain from dense point clouds. The MSVC performance on two dense point clouds, capturing highly rugged areas with dense vegetation cover, was compared with that of the widely used cloth simulation filter (CSF) using manually classified terrain as the reference. MSVC consistently outperformed the CSF filter in terms of the correctly identified ground points, correctly identified non-ground points, balanced accuracy, and the F-score. Another advantage of this filter lay in its easy adaptability to any type of terrain, enabled by the utilization of machine learning. The only disadvantage lay in the necessity to manually prepare training data. On the other hand, we aim to account for this in the future by producing neural networks trained for individual landscape types, thus eliminating this phase of the work.https://www.mdpi.com/2072-4292/17/4/615point cloudground filteringneural networkmachine learning |
| spellingShingle | Martin Štroner Martin Boušek Jakub Kučera Hana Váchová Rudolf Urban Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network Remote Sensing point cloud ground filtering neural network machine learning |
| title | Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network |
| title_full | Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network |
| title_fullStr | Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network |
| title_full_unstemmed | Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network |
| title_short | Multi-Size Voxel Cube (MSVC) Algorithm—A Novel Method for Terrain Filtering from Dense Point Clouds Using a Deep Neural Network |
| title_sort | multi size voxel cube msvc algorithm a novel method for terrain filtering from dense point clouds using a deep neural network |
| topic | point cloud ground filtering neural network machine learning |
| url | https://www.mdpi.com/2072-4292/17/4/615 |
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