ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 197-204, 2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
07 Aug 2014
Building modeling from noisy photogrammetric point clouds
B. Xiong, S. Oude Elberink, and G. Vosselman ITC, University of Twente, Enschede, the Netherlands
Keywords: 3D Building Reconstruction, Photogrammetric point cloud, Structure boundaries and points Abstract. 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.
Conference paper (PDF, 2641 KB)

Citation: Xiong, B., Oude Elberink, S., and Vosselman, G.: Building modeling from noisy photogrammetric point clouds, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3, 197-204, doi:10.5194/isprsannals-II-3-197-2014, 2014.

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