City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds
We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/9/2254 |
| _version_ | 1797502985795796992 |
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| author | Jin Huang Jantien Stoter Ravi Peters Liangliang Nan |
| author_facet | Jin Huang Jantien Stoter Ravi Peters Liangliang Nan |
| author_sort | Jin Huang |
| collection | DOAJ |
| description | We present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications. |
| first_indexed | 2024-03-10T03:44:01Z |
| format | Article |
| id | doaj.art-82d3e8c3b419432ca95c7f94a3e7f4be |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T03:44:01Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-82d3e8c3b419432ca95c7f94a3e7f4be2023-11-23T09:12:56ZengMDPI AGRemote Sensing2072-42922022-05-01149225410.3390/rs14092254City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point CloudsJin Huang0Jantien Stoter1Ravi Peters2Liangliang Nan33D Geoinformation Research Group, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands3D Geoinformation Research Group, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands3D Geoinformation Research Group, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The Netherlands3D Geoinformation Research Group, Faculty of Architecture and the Built Environment, Delft University of Technology, 2628 BL Delft, The NetherlandsWe present a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. A major challenge of urban reconstruction from airborne LiDAR point clouds lies in that the vertical walls are typically missing. Based on the observation that urban buildings typically consist of planar roofs connected with vertical walls to the ground, we propose an approach to infer the vertical walls directly from the data. With the planar segments of both roofs and walls, we hypothesize the faces of the building surface, and the final model is obtained by using an extended hypothesis-and-selection-based polygonal surface reconstruction framework. Specifically, we introduce a new energy term to encourage roof preferences and two additional hard constraints into the optimization step to ensure correct topology and enhance detail recovery. Experiments on various large-scale airborne LiDAR point clouds have demonstrated that the method is superior to the state-of-the-art methods in terms of reconstruction accuracy and robustness. In addition, we have generated a new dataset with our method consisting of the point clouds and 3D models of 20k real-world buildings. We believe this dataset can stimulate research in urban reconstruction from airborne LiDAR point clouds and the use of 3D city models in urban applications.https://www.mdpi.com/2072-4292/14/9/2254building reconstructionLiDARpoint cloudsinteger programming |
| spellingShingle | Jin Huang Jantien Stoter Ravi Peters Liangliang Nan City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds Remote Sensing building reconstruction LiDAR point clouds integer programming |
| title | City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds |
| title_full | City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds |
| title_fullStr | City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds |
| title_full_unstemmed | City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds |
| title_short | City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds |
| title_sort | city3d large scale building reconstruction from airborne lidar point clouds |
| topic | building reconstruction LiDAR point clouds integer programming |
| url | https://www.mdpi.com/2072-4292/14/9/2254 |
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