The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Algorithm for Finding Best Matches in Logarithmic Expected Time
ACM Transactions on Mathematical Software (TOMS)
Information Retrieval
Combining Appearance and Topology for Wide Baseline Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Wide Baseline Point Matching Using Affine Invariants Computed from Intensity Profiles
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Automatic Panoramic Image Stitching using Invariant Features
International Journal of Computer Vision
Image-based procedural modeling of facades
ACM SIGGRAPH 2007 papers
Analysis of Building Textures for Reconstructing Partially Occluded Facades
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Image-based street-side city modeling
ACM SIGGRAPH Asia 2009 papers
Morphological segmentation of building façade images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Gable roof detection in terrestrial images
PIA'11 Proceedings of the 2011 ISPRS conference on Photogrammetric image analysis
Sorting unorganized photo sets for urban reconstruction
Graphical Models
Learning domain knowledge for façade labelling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
A three-layered approach to facade parsing
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Building facade detection, segmentation, and parameter estimation for mobile robot stereo vision
Image and Vision Computing
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We introduce a novel approach for separating and segmenting individual facades from streetside images. Our algorithm incorporates prior knowledge about arbitrarily shaped repetitive regions which are detected using intensity profile descriptors and a voting-based matcher. In the experiments we compare our approach to extended state-of-the-art matching approaches using more than 600 challenging streetside images, including different building styles and various occlusions. Our algorithm outperforms these approaches and allows to correctly separate 94% of the facades. Pixel-wise comparison to our ground-truth yields a segmentation accuracy of 85%. According to these results our work is an important contribution to fully automatic building reconstruction.