Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding
Indoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. To achieve semantic u...
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| Language: | English |
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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/2072-4292/17/4/596 |
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| author | Minglei Li Mingfan Li Min Li Leheng Xu |
| author_facet | Minglei Li Mingfan Li Min Li Leheng Xu |
| author_sort | Minglei Li |
| collection | DOAJ |
| description | Indoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. To achieve semantic understanding, we propose an effective 3D instance segmentation module using a deep network Indoor3DNet combined with super-point clustering, which provides a larger receptive field and maintains the continuity of individual objects. The Indoor3DNet includes an efficient point feature extraction backbone with good operability for different object granularity. In addition, we use a geometric primitives-based modeling approach to generate lightweight polygonal facets for walls and use a cross-modal registration technique to fit the corresponding instance models for internal objects based on their semantic labels. This modeling method can restore correct geometric shapes and topological relationships while maintaining a very lightweight structure. We have tested the method on diverse datasets, and the experimental results demonstrate that the method outperforms the state-of-the-art in terms of performance and robustness. |
| format | Article |
| id | doaj-art-fe377cbdc5d54ca485791cf34ec86556 |
| institution | DOAJ |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-fe377cbdc5d54ca485791cf34ec865562025-08-20T02:44:36ZengMDPI AGRemote Sensing2072-42922025-02-0117459610.3390/rs17040596Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene UnderstandingMinglei Li0Mingfan Li1Min Li2Leheng Xu3College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaCollege of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, ChinaIndoor scenes often contain complex layouts and interactions between objects, making 3D modeling of point clouds inherently difficult. In this paper, we design a divide-and-conquer modeling method considering the structural differences between indoor walls and internal objects. To achieve semantic understanding, we propose an effective 3D instance segmentation module using a deep network Indoor3DNet combined with super-point clustering, which provides a larger receptive field and maintains the continuity of individual objects. The Indoor3DNet includes an efficient point feature extraction backbone with good operability for different object granularity. In addition, we use a geometric primitives-based modeling approach to generate lightweight polygonal facets for walls and use a cross-modal registration technique to fit the corresponding instance models for internal objects based on their semantic labels. This modeling method can restore correct geometric shapes and topological relationships while maintaining a very lightweight structure. We have tested the method on diverse datasets, and the experimental results demonstrate that the method outperforms the state-of-the-art in terms of performance and robustness.https://www.mdpi.com/2072-4292/17/4/5963D indoor modelssemantic segmentationpoint cloud clusteringIndoor3DNet |
| spellingShingle | Minglei Li Mingfan Li Min Li Leheng Xu Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding Remote Sensing 3D indoor models semantic segmentation point cloud clustering Indoor3DNet |
| title | Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding |
| title_full | Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding |
| title_fullStr | Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding |
| title_full_unstemmed | Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding |
| title_short | Building Lightweight 3D Indoor Models from Point Clouds with Enhanced Scene Understanding |
| title_sort | building lightweight 3d indoor models from point clouds with enhanced scene understanding |
| topic | 3D indoor models semantic segmentation point cloud clustering Indoor3DNet |
| url | https://www.mdpi.com/2072-4292/17/4/596 |
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