Generation of Structural Components for Indoor Spaces from Point Clouds
Point clouds from laser scanners have been widely used in recent research on indoor modeling methods. Currently, particularly in data-driven modeling methods, data preprocessing for dividing structural components and nonstructural components is required before modeling. In this paper, we propose an...
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MDPI AG
2025-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/10/3012 |
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| author | Junhyuk Lee Yutaka Ohtake Takashi Nakano Daisuke Sato |
| author_facet | Junhyuk Lee Yutaka Ohtake Takashi Nakano Daisuke Sato |
| author_sort | Junhyuk Lee |
| collection | DOAJ |
| description | Point clouds from laser scanners have been widely used in recent research on indoor modeling methods. Currently, particularly in data-driven modeling methods, data preprocessing for dividing structural components and nonstructural components is required before modeling. In this paper, we propose an indoor modeling method without the classification of structural and nonstructural components. A pre-mesh is generated for constructing the adjacency relations of point clouds, and plane components are extracted using planar-based region growing. Then, the distance fields of each plane are calculated, and voxel data referred to as a surface confidence map are obtained. Subsequently, the inside and outside of the indoor model are classified using a graph-cut algorithm. Finally, indoor models with watertight meshes are generated via dual contouring and mesh refinement. The experimental results showed that the point-to-mesh error ranged from approximately 2 mm to 50 mm depending on the dataset. Furthermore, completeness—measured as the proportion of original point-cloud data successfully reconstructed into the mesh—approached 1.0 for single-room datasets and reached around 0.95 for certain multiroom and synthetic datasets. These results demonstrate the effectiveness of the proposed method in automatically removing non-structural components and generating clean structural meshes. |
| format | Article |
| id | doaj-art-2210960b5ccb410e97dd9b51c657a1c0 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-2210960b5ccb410e97dd9b51c657a1c02025-08-20T03:12:15ZengMDPI AGSensors1424-82202025-05-012510301210.3390/s25103012Generation of Structural Components for Indoor Spaces from Point CloudsJunhyuk Lee0Yutaka Ohtake1Takashi Nakano2Daisuke Sato3School of Precision Engineering, The University of Tokyo, Tokyo 113-8654, JapanSchool of Precision Engineering, The University of Tokyo, Tokyo 113-8654, JapanDataLabs, Inc., Tokyo 103-0024, JapanDataLabs, Inc., Tokyo 103-0024, JapanPoint clouds from laser scanners have been widely used in recent research on indoor modeling methods. Currently, particularly in data-driven modeling methods, data preprocessing for dividing structural components and nonstructural components is required before modeling. In this paper, we propose an indoor modeling method without the classification of structural and nonstructural components. A pre-mesh is generated for constructing the adjacency relations of point clouds, and plane components are extracted using planar-based region growing. Then, the distance fields of each plane are calculated, and voxel data referred to as a surface confidence map are obtained. Subsequently, the inside and outside of the indoor model are classified using a graph-cut algorithm. Finally, indoor models with watertight meshes are generated via dual contouring and mesh refinement. The experimental results showed that the point-to-mesh error ranged from approximately 2 mm to 50 mm depending on the dataset. Furthermore, completeness—measured as the proportion of original point-cloud data successfully reconstructed into the mesh—approached 1.0 for single-room datasets and reached around 0.95 for certain multiroom and synthetic datasets. These results demonstrate the effectiveness of the proposed method in automatically removing non-structural components and generating clean structural meshes.https://www.mdpi.com/1424-8220/25/10/30123D indoor modeling3D reconstructionplanar-based region growinggraph-cutunsigned distance fields |
| spellingShingle | Junhyuk Lee Yutaka Ohtake Takashi Nakano Daisuke Sato Generation of Structural Components for Indoor Spaces from Point Clouds Sensors 3D indoor modeling 3D reconstruction planar-based region growing graph-cut unsigned distance fields |
| title | Generation of Structural Components for Indoor Spaces from Point Clouds |
| title_full | Generation of Structural Components for Indoor Spaces from Point Clouds |
| title_fullStr | Generation of Structural Components for Indoor Spaces from Point Clouds |
| title_full_unstemmed | Generation of Structural Components for Indoor Spaces from Point Clouds |
| title_short | Generation of Structural Components for Indoor Spaces from Point Clouds |
| title_sort | generation of structural components for indoor spaces from point clouds |
| topic | 3D indoor modeling 3D reconstruction planar-based region growing graph-cut unsigned distance fields |
| url | https://www.mdpi.com/1424-8220/25/10/3012 |
| work_keys_str_mv | AT junhyuklee generationofstructuralcomponentsforindoorspacesfrompointclouds AT yutakaohtake generationofstructuralcomponentsforindoorspacesfrompointclouds AT takashinakano generationofstructuralcomponentsforindoorspacesfrompointclouds AT daisukesato generationofstructuralcomponentsforindoorspacesfrompointclouds |