Geometry and texture recovery of scenes of large scale
Computer Vision and Image Understanding
Robust Regression with Projection Based M-estimators
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Discriminative Learning of Markov Random Fields for Segmentation of 3D Scan Data
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Using Extended EM to Segment Planar Structures in 3D
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
3D Modeling Using Planar Segments and Mesh Elements
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Finite sample bias of robust scale estimators in computer vision problems
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part I
A vehicle-borne urban 3-D acquisition system using single-row laser range scanners
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Range image segmentation using surface selection criterion
IEEE Transactions on Image Processing
An M-estimator for high breakdown robust estimation in computer vision
Computer Vision and Image Understanding
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Complex multiple structures, high uncertainty due to the existence of moving objects, and significant disparity in the size of features are the main issues associated with processing range data of outdoor scenes. The existing range segmentation techniques have been commonly developed for laboratory sized objects or simple architectural building features. In this paper, main problems related to the geometrical segmentation of large and significant buildings are studied. A robust and accurate range segmentation approach is also devised to extract very fine geometric details of building exteriors. It uses a hierarchical model-base range segmentation strategy and employs a high breakdown point robust estimator to deal with the existing discrepancies in size and sampling rates of various features of large outdoor objects. The proposed range segmentation algorithm facilitates automatic generation of fine 3D models of environment. The computational advantages and segmentation capabilities of the proposed method are shown using real range data of large building exteriors.