Issue No. 10 - Oct. (2012 vol. 18)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TVCG.2012.30
Carlos A. Vanegas , Purdue University, West Lafayette
Daniel G. Aliaga , Purdue University, West Lafayette
Bedrich Benes , Purdue University, West Lafayette
We propose a novel approach for the reconstruction of urban structures from 3D point clouds with an assumption of Manhattan World (MW) building geometry; i.e., the predominance of three mutually orthogonal directions in the scene. Our approach works in two steps. First, the input points are classified according to the MW assumption into four local shape types: walls, edges, corners, and edge corners. The classified points are organized into a connected set of clusters from which a volume description is extracted. The MW assumption allows us to robustly identify the fundamental shape types, describe the volumes within the bounding box, and reconstruct visible and occluded parts of the sampled structure. We show results of our reconstruction that has been applied to several synthetic and real-world 3D point data sets of various densities and from multiple viewpoints. Our method automatically reconstructs 3D building models from up to 10 million points in 10 to 60 seconds.
Shape analysis, Three dimensional displays, Image reconstruction, Geometry, Robustness, Manhattan world., 3D Modeling, 3D reconstruction, laser scans, buildings
C. A. Vanegas, D. G. Aliaga and B. Benes, "Automatic Extraction of Manhattan-World Building Masses from 3D Laser Range Scans," in IEEE Transactions on Visualization & Computer Graphics, vol. 18, no. , pp. 1627-1637, 2012.