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The Multi-View Stereo (MVS) technology has improved significantly in the last decade, providing a much denser and more accuratepoint cloud than before. The point cloud now becomes a valuable data for modelling the LOD2 buildings. However, it is still notaccurate 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 quiteerroneous and cannot be used to model the buildings using the current methods based on roof topology graphs. We propose aparameter-free algorithm to robustly and precisely derive roof structures and building models. The points connecting roof segmentsare searched and grouped as structure points and structure boundaries, accordingly presenting the roof corners and boundaries. Theirgeometries are computed by the plane equations of their attached roof segments. If data available, the algorithm guarantees completebuilding structures in noisy point clouds and meanwhile achieves global optimized models. Experiments show that, when comparingto the roof topology graph based methods, the novel algorithm achieves consistent quality for both lidar and photogrammetric pointclouds. But the new method is fully automatic and is a good alternative for the model-driven method when the processing time isimportant

Building modeling from noisy photogrammetric point clouds