International Journal of Computer Vision

, Volume 117, Issue 2, pp 111–130 | Cite as

Vanishing Point Estimation and Line Classification in a Manhattan World with a Unifying Camera Model

  • Lilian Zhang
  • Huimin Lu
  • Xiaoping Hu
  • Reinhard Koch


The problem of estimating vanishing points for visual scenes under the Manhattan world assumption has been addressed for more than a decade. Surprisingly, the special characteristic of the Manhattan world that lines should be orthogonal or parallel to each other is seldom well utilized. In this paper, we present an algorithm that accurately and efficiently estimates vanishing points and classifies lines by thoroughly taking advantage of this simple fact in the Manhattan world for images grabbed by a camera with a single effective viewpoint (e.g. perspective camera or central catadioptric camera). The algorithm is also extended to estimate the focal length of the camera when it is uncalibrated. The key novelty is to estimate three orthogonal line directions in the camera frame simultaneously instead of estimating vanishing points in the image plane directly. The performance of the proposed algorithm is demonstrated on four publicly available databases. Compared to the state-of-the-art methods, the experiments show its superiority in terms of both accuracy and efficiency.


Vanishing points Line classification Manhattan world Unifying camera model 



This work was supported by China Scholarship Council (No. 2009611008) and National Natural Science Foundation of China (No. 61503403). The authors thank the anonymous reviewers for their valuable comments.

Supplementary material

11263_2015_854_MOESM1_ESM.pdf (28.3 mb)
Supplementary material 1 (pdf 28979 KB)
11263_2015_854_MOESM2_ESM.pdf (13.6 mb)
Supplementary material 2 (pdf 13905 KB)
11263_2015_854_MOESM3_ESM.mpg (60 mb)
Supplementary material 3 (mpg 61446 KB)
11263_2015_854_MOESM4_ESM.mpg (173.4 mb)
Supplementary material 4 (mpg 177574 KB)


  1. Aguilera, D. G., Lahoz, J. G., & Codes, J. F. (2005). A new method for vanishing points detection in 3d reconstruction from a single view. In ISPRS.Google Scholar
  2. Baker, S., & Nayar, S. (1998). A theory of catadioptric image formation. In ICCV (pp. 35–42).Google Scholar
  3. Barreto, J., & Araujo, H. (2005). Geometric properties of central catadioptric line images and their application in calibration. TPAMI, 27(8), 1327–1333.CrossRefGoogle Scholar
  4. Bazin, J. C., & Pollefeys, M. (2012). 3-line RANSAC for orthogonal vanishing point detection. In IROS (pp. 4282–4287).Google Scholar
  5. Bazin, J. C., Demonceaux, C., Vasseur, P., & Kweon, I. S. (2010). Motion estimation by decoupling rotation and translation in catadioptric vision. CVIU, 114(2), 254–273.Google Scholar
  6. Bazin, J. C., Demonceaux, C., Vasseur, P., & Kweon, I. (2012a). Rotation estimation and vanishing point extraction by omnidirectional vision in urban environment. IJRR, 31(1), 63–81.Google Scholar
  7. Bazin, J. C., Seo, Y., & Pollefeys, M. (2012b). Globally optimal line clustering and vanishing point estimation in manhattan world. In CVPR.Google Scholar
  8. Ceriani, S., Fontana, G., Giusti, A., Marzorati, D., Matteucci, M., Migliore, D., et al. (2009). Rawseeds ground truth collection systems for indoor self-localization and mapping. Autonomous Robots, 27(4), 353–371.CrossRefGoogle Scholar
  9. Chen, H. (1991). Pose determination from line-to-plane correspondences: Existence condition and closed-form solutions. TPAMI, 13, 530–541.CrossRefGoogle Scholar
  10. Cipolla, R., Drummond, T., & Robertson, D. (1999). Camera calibration from vanishing points in images of architectural scenes. In BMVC (pp. 382–392).Google Scholar
  11. Coughlan, J. M., & Yuille, A. L. (1999). Manhattan world: Compass direction from a single image by bayesian inference. In ICCV (pp. 941–947).Google Scholar
  12. Coughlan, J. M., & Yuille, A. L. (2003). Manhattan world: Orientation and outlier detection by bayesian inference. Neural Computation, 15(5), 1063–1088.CrossRefGoogle Scholar
  13. Denis, P., Elder, J. H., & Estrada, F. J. (2008). Efficient edge-based methods for estimating manhattan frames in urban imagery. In ECCV (pp. 197–210).Google Scholar
  14. Flint, A., Mei, C., Reid, I., & Murray, D. (2010). Growing semantically meaningful models for visual slam. In CVPR (pp. 467–474).Google Scholar
  15. Förstner, W. (2010). Optimal vanishing point detection and rotation estimation of single images of a legolandscene. In ISPRS.Google Scholar
  16. Gallagher, A. C. (2002). A ground truth based vanishing point detection algorithm. Pattern Recognition, 35(7), 1527–1543.CrossRefzbMATHGoogle Scholar
  17. Geyer, C., & Daniilidis, K. (2001). Catadioptric projective geometry. IJCV, 45, 223–243.CrossRefzbMATHGoogle Scholar
  18. Hartley, R. I., & Kahl, F. (2009). Global optimization through rotation space search. IJCV, 82(1), 64–79.CrossRefGoogle Scholar
  19. Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed.). Cambridge: Cambridge University Press.CrossRefzbMATHGoogle Scholar
  20. Kessler, C., Ascher, C., Frietsch, N., Weinmann, M., & Trommer, G. (2010). Vision-based attitude estimation for indoor navigation using vanishing points and lines. In PLANS (pp. 310–318).Google Scholar
  21. Kosecka, J., & Zhang, W. (2002) Video compass. In ECCV (pp. 657–673).Google Scholar
  22. Lee, D. C., Hebert, M., & Kanade, T. (2009). Geometric reasoning for single image structure recovery. In CVPR (pp. 2136–2143).Google Scholar
  23. Liebowitz, D. (2001). Camera calibration and reconstruction of geometry from images. PhD thesis, Department Engineering Science, University of Oxford.Google Scholar
  24. Liebowitz, D., & Zisserman, A. (1998). Metric rectification for perspective images of planes. In CVPR (pp. 482–488).Google Scholar
  25. Lutton, E., Maitre, H., & Lopez-Krahe, J. (1994). Contribution to the determination of vanishing points using hough transform. TPAMI, 16(4), 430–438.CrossRefGoogle Scholar
  26. Mei, C. (2007). Laser-augmented omnidirectional vision for 3D localisation and mapping. PhD thesis, INRIA Sophia Antipolis, Project-team ARobAS.Google Scholar
  27. Mirzaei, F. M., & Roumeliotis, S. I. (2011). Optimal estimation of vanishing points in a manhattan world. In ICCV (pp. 2454–2461).Google Scholar
  28. Nieto, M., & Salgado, L. (2011). Simultaneous estimation of vanishing points and their converging lines using the em algorithm. Pattern Recognition Letters, 32(14), 1691–1700.CrossRefGoogle Scholar
  29. Nocedal, J., & Wright, S. J. (2006). Numerical optimization (2nd ed.). New York: Springer.zbMATHGoogle Scholar
  30. Pronobis, A., & Caputo, B. (2009). Cold: The cosy localization database. IJRR, Special Issue on Robotic Vision, 28(5), 588–594.Google Scholar
  31. Rother, C. (2000). A new approach for vanishing point detection in architectural environments. In BMVC (pp. 382–391).Google Scholar
  32. Schindler, G., & Dellaert, F. (2004). Atlanta world: An expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In CVPR (pp. 203–209).Google Scholar
  33. Tardif, J. P. (2009). Non-iterative approach for fast and accurate vanishing point detection. In ICCV (pp. 1250–1257).Google Scholar
  34. Tretyak, E., Barinova, O., Kohli, P., & Lempitsky, V. (2012). Geometric image parsing in man-made environments. IJCV, 97, 305–321.Google Scholar
  35. Tuytelaars, T., Van Gool L., Proesmans, M., & Moons, T. (1998) The cascaded hough transform as an aid in aerial image interpretation. In ICCV (pp. 67–72).Google Scholar
  36. von Gioi, R. G., Jakubowicz, J., Morel, J. M., & Randall, G. (2010). Lsd: A fast line segment detector with a false detection control. TPAMI, 32, 722–732.CrossRefGoogle Scholar
  37. Wan, G., & Li, S. (2011). Automatic facades segmentation using detected lines and vanishing points. In CISP (pp. 1214–1217).Google Scholar
  38. Wildenauer, H., & Hanbury, A. (2012). Robust camera self-calibration from monocular images of manhattan worlds. In CVPR (pp. 2831–2838).Google Scholar
  39. Wildenauer, H., & Vincze, M. (2007). Vanishing point detection in complex man-made worlds. In ICIAP (pp. 615–622).Google Scholar
  40. Zhang, L., & Koch, R. (2011). Hand-held monocular slam based on line segments. In IMVIP (pp. 8–15).Google Scholar
  41. Zhang, L., & Koch, R. (2012). Vanishing points estimation and line classification in a manhattan world. In ACCV, Part II (pp. 38–45).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Lilian Zhang
    • 1
  • Huimin Lu
    • 1
  • Xiaoping Hu
    • 1
  • Reinhard Koch
    • 2
  1. 1.Department of Automatic Control, College of Mechatronics and AutomationNational University of Defense TechnologyChangshaChina
  2. 2.Institute of Computer ScienceUniversity of KielKielGermany

Personalised recommendations