ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W3, 73-78, 2013
http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W3/73/2013/
doi:10.5194/isprsannals-II-3-W3-73-2013
© Author(s) 2013. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
08 Oct 2013
Roof Reconstruction from Point Clouds using Importance Sampling
W. Nguatem, M. Drauschke, and H. Mayer University of Bundeswehr, Munich, Institute of Applied Computer Science, Chair for Visual Computing, Neubiberg, Germany
Keywords: Building Reconstruction, Point Cloud Segmentation, MCMC, Model Selection, Non-linear Regression Fitting Abstract. We propose a novel fully automatic technique for roof fitting in 3D point clouds based on sequential importance sampling (SIS). Our approach makes no assumption of the nature (sparse, dense) or origin (LIDAR, image matching) of the point clouds and further distinguishes, automatically, between different basic roof types based on model selection. The algorithm comprises an inherent data parallelism, the lack of which has been a major drawback of most Monte Carlo schemes. A further speedup is achieved by applying a coarse to fine search within all probable roof configurations in the sample space of roofs. The robustness and effectiveness of our roof reconstruction algorithm is illustrated for point clouds of varying nature.
Conference paper (PDF, 1448 KB)


Citation: Nguatem, W., Drauschke, M., and Mayer, H.: Roof Reconstruction from Point Clouds using Importance Sampling, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W3, 73-78, doi:10.5194/isprsannals-II-3-W3-73-2013, 2013.

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