Vanishing Point Detection by Segment Clustering on the Projective Space
The analysis of vanishing points on digital images provides strong cues for inferring the 3D structure of the depicted scene and can be exploited in a variety of computer vision applications. In this paper, we propose a method for estimating vanishing points in images of architectural environments that can be used for camera calibration and pose estimation, important tasks in large-scale 3D reconstruction. Our method performs automatic segment clustering in projective space – a direct transformation from the image space – instead of the traditional bounded accumulator space. Since it works in projective space, it handles finite and infinite vanishing points, without any special condition or threshold tuning. Experiments on real images show the effectiveness of the proposed method. We identify three orthogonal vanishing points and compute the estimation error based on their relation with the Image of the Absolute Conic (IAC) and based on the computation of the camera focal length.
KeywordsVanishing point detection Segment clustering 3D reconstruction
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- 1.Zeng, X., Wang, Q., Xu, J.: MAP Model for Large-scale 3D Reconstruction and Coarse Matching for Unordered Wide-baseline Photos. In: British Machine Vision Conference (2008)Google Scholar
- 3.Lee, S.C., Jung, S.K., Nevatia, R.: Automatic Integration of Facade Textures into 3D Building Models with a Projective Geometry Based Line Clustering. In: USC Computer Vision (2002)Google Scholar
- 11.Tuytelaars, T., Van Gool, L.J., Proesmans, M., Moons, T.: A Cascaded Hough Transform as an Aid in Aerial Image Interpretation. In: International Conference on Computer Vision, pp. 67–72 (1998)Google Scholar
- 14.Rother, C.: A New Approach for Vanishing Point Detection in Architectural Environments. In: British Machine Vision Conference (2000)Google Scholar
- 16.Tardif, J.-P.: Non-Iterative Approach for Fast and Accurate Vanishing Point Detection. In: International Conference on Computer Vision, pp. 1250–1257 (2009)Google Scholar
- 18.Stolfi, J.: Oriented Projective Geometry: A Framework for Geometric Computations. Academic Press (1991)Google Scholar
- 19.Mardia, K.V., Jupp, P.E.: Directional statistics. John Wiley and Sons (1999)Google Scholar