Abstract
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.
Similar content being viewed by others
References
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.
Baker, S., & Nayar, S. (1998). A theory of catadioptric image formation. In ICCV (pp. 35–42).
Barreto, J., & Araujo, H. (2005). Geometric properties of central catadioptric line images and their application in calibration. TPAMI, 27(8), 1327–1333.
Bazin, J. C., & Pollefeys, M. (2012). 3-line RANSAC for orthogonal vanishing point detection. In IROS (pp. 4282–4287).
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.
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.
Bazin, J. C., Seo, Y., & Pollefeys, M. (2012b). Globally optimal line clustering and vanishing point estimation in manhattan world. In CVPR.
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.
Chen, H. (1991). Pose determination from line-to-plane correspondences: Existence condition and closed-form solutions. TPAMI, 13, 530–541.
Cipolla, R., Drummond, T., & Robertson, D. (1999). Camera calibration from vanishing points in images of architectural scenes. In BMVC (pp. 382–392).
Coughlan, J. M., & Yuille, A. L. (1999). Manhattan world: Compass direction from a single image by bayesian inference. In ICCV (pp. 941–947).
Coughlan, J. M., & Yuille, A. L. (2003). Manhattan world: Orientation and outlier detection by bayesian inference. Neural Computation, 15(5), 1063–1088.
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).
Flint, A., Mei, C., Reid, I., & Murray, D. (2010). Growing semantically meaningful models for visual slam. In CVPR (pp. 467–474).
Förstner, W. (2010). Optimal vanishing point detection and rotation estimation of single images of a legolandscene. In ISPRS.
Gallagher, A. C. (2002). A ground truth based vanishing point detection algorithm. Pattern Recognition, 35(7), 1527–1543.
Geyer, C., & Daniilidis, K. (2001). Catadioptric projective geometry. IJCV, 45, 223–243.
Hartley, R. I., & Kahl, F. (2009). Global optimization through rotation space search. IJCV, 82(1), 64–79.
Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision (2nd ed.). Cambridge: Cambridge University Press.
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).
Kosecka, J., & Zhang, W. (2002) Video compass. In ECCV (pp. 657–673).
Lee, D. C., Hebert, M., & Kanade, T. (2009). Geometric reasoning for single image structure recovery. In CVPR (pp. 2136–2143).
Liebowitz, D. (2001). Camera calibration and reconstruction of geometry from images. PhD thesis, Department Engineering Science, University of Oxford.
Liebowitz, D., & Zisserman, A. (1998). Metric rectification for perspective images of planes. In CVPR (pp. 482–488).
Lutton, E., Maitre, H., & Lopez-Krahe, J. (1994). Contribution to the determination of vanishing points using hough transform. TPAMI, 16(4), 430–438.
Mei, C. (2007). Laser-augmented omnidirectional vision for 3D localisation and mapping. PhD thesis, INRIA Sophia Antipolis, Project-team ARobAS.
Mirzaei, F. M., & Roumeliotis, S. I. (2011). Optimal estimation of vanishing points in a manhattan world. In ICCV (pp. 2454–2461).
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.
Nocedal, J., & Wright, S. J. (2006). Numerical optimization (2nd ed.). New York: Springer.
Pronobis, A., & Caputo, B. (2009). Cold: The cosy localization database. IJRR, Special Issue on Robotic Vision, 28(5), 588–594.
Rother, C. (2000). A new approach for vanishing point detection in architectural environments. In BMVC (pp. 382–391).
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).
Tardif, J. P. (2009). Non-iterative approach for fast and accurate vanishing point detection. In ICCV (pp. 1250–1257).
Tretyak, E., Barinova, O., Kohli, P., & Lempitsky, V. (2012). Geometric image parsing in man-made environments. IJCV, 97, 305–321.
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).
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.
Wan, G., & Li, S. (2011). Automatic facades segmentation using detected lines and vanishing points. In CISP (pp. 1214–1217).
Wildenauer, H., & Hanbury, A. (2012). Robust camera self-calibration from monocular images of manhattan worlds. In CVPR (pp. 2831–2838).
Wildenauer, H., & Vincze, M. (2007). Vanishing point detection in complex man-made worlds. In ICIAP (pp. 615–622).
Zhang, L., & Koch, R. (2011). Hand-held monocular slam based on line segments. In IMVIP (pp. 8–15).
Zhang, L., & Koch, R. (2012). Vanishing points estimation and line classification in a manhattan world. In ACCV, Part II (pp. 38–45).
Acknowledgments
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.
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by Cordelia Schmid.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Zhang, L., Lu, H., Hu, X. et al. Vanishing Point Estimation and Line Classification in a Manhattan World with a Unifying Camera Model. Int J Comput Vis 117, 111–130 (2016). https://doi.org/10.1007/s11263-015-0854-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11263-015-0854-5