Building modeling from noisy photogrammetric point clouds
The Multi-View Stereo (MVS) technology has improved significantly in the last decade, providing a much denser and more accurate point cloud than before. The point cloud now becomes a valuable data for modelling the LOD2 buildings. However, it is still not accurate enough to replace the lidar point c...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2014-08-01
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| Series: | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3/197/2014/isprsannals-II-3-197-2014.pdf |
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| author | B. Xiong S. Oude Elberink G. Vosselman |
| author_facet | B. Xiong S. Oude Elberink G. Vosselman |
| author_sort | B. Xiong |
| collection | DOAJ |
| description | The Multi-View Stereo (MVS) technology has improved significantly in the last decade, providing a much denser and more accurate
point cloud than before. The point cloud now becomes a valuable data for modelling the LOD2 buildings. However, it is still not
accurate enough to replace the lidar point cloud. Its relative high level of noise prevents the accurate interpretation of roof faces, e.g.
one planar roof face has uneven surface of points therefore is segmented into many parts. The derived roof topology graphs are quite
erroneous and cannot be used to model the buildings using the current methods based on roof topology graphs. We propose a
parameter-free algorithm to robustly and precisely derive roof structures and building models. The points connecting roof segments
are searched and grouped as structure points and structure boundaries, accordingly presenting the roof corners and boundaries. Their
geometries are computed by the plane equations of their attached roof segments. If data available, the algorithm guarantees complete
building structures in noisy point clouds and meanwhile achieves global optimized models. Experiments show that, when comparing
to the roof topology graph based methods, the novel algorithm achieves consistent quality for both lidar and photogrammetric point
clouds. But the new method is fully automatic and is a good alternative for the model-driven method when the processing time is
important. |
| first_indexed | 2024-12-11T05:27:21Z |
| format | Article |
| id | doaj.art-023facf912c64e61a50bd807d0c64ea7 |
| institution | Directory Open Access Journal |
| issn | 2194-9042 2194-9050 |
| language | English |
| last_indexed | 2024-12-11T05:27:21Z |
| publishDate | 2014-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| spelling | doaj.art-023facf912c64e61a50bd807d0c64ea72022-12-22T01:19:32ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502014-08-01II-319720410.5194/isprsannals-II-3-197-2014Building modeling from noisy photogrammetric point cloudsB. Xiong0S. Oude Elberink1G. Vosselman2ITC, University of Twente, Enschede, the NetherlandsITC, University of Twente, Enschede, the NetherlandsITC, University of Twente, Enschede, the NetherlandsThe Multi-View Stereo (MVS) technology has improved significantly in the last decade, providing a much denser and more accurate point cloud than before. The point cloud now becomes a valuable data for modelling the LOD2 buildings. However, it is still not accurate enough to replace the lidar point cloud. Its relative high level of noise prevents the accurate interpretation of roof faces, e.g. one planar roof face has uneven surface of points therefore is segmented into many parts. The derived roof topology graphs are quite erroneous and cannot be used to model the buildings using the current methods based on roof topology graphs. We propose a parameter-free algorithm to robustly and precisely derive roof structures and building models. The points connecting roof segments are searched and grouped as structure points and structure boundaries, accordingly presenting the roof corners and boundaries. Their geometries are computed by the plane equations of their attached roof segments. If data available, the algorithm guarantees complete building structures in noisy point clouds and meanwhile achieves global optimized models. Experiments show that, when comparing to the roof topology graph based methods, the novel algorithm achieves consistent quality for both lidar and photogrammetric point clouds. But the new method is fully automatic and is a good alternative for the model-driven method when the processing time is important.http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3/197/2014/isprsannals-II-3-197-2014.pdf |
| spellingShingle | B. Xiong S. Oude Elberink G. Vosselman Building modeling from noisy photogrammetric point clouds ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| title | Building modeling from noisy photogrammetric point clouds |
| title_full | Building modeling from noisy photogrammetric point clouds |
| title_fullStr | Building modeling from noisy photogrammetric point clouds |
| title_full_unstemmed | Building modeling from noisy photogrammetric point clouds |
| title_short | Building modeling from noisy photogrammetric point clouds |
| title_sort | building modeling from noisy photogrammetric point clouds |
| url | http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3/197/2014/isprsannals-II-3-197-2014.pdf |
| work_keys_str_mv | AT bxiong buildingmodelingfromnoisyphotogrammetricpointclouds AT soudeelberink buildingmodelingfromnoisyphotogrammetricpointclouds AT gvosselman buildingmodelingfromnoisyphotogrammetricpointclouds |