Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10496)

Abstract

We present a novel approach for vanishing point detection from uncalibrated monocular images. In contrast to state-of-the-art, we make no a priori assumptions about the observed scene. Our method is based on a convolutional neural network (CNN) which does not use natural images, but a Gaussian sphere representation arising from an inverse gnomonic projection of lines detected in an image. This allows us to rely on synthetic data for training, eliminating the need for labelled images. Our method achieves competitive performance on three horizon estimation benchmark datasets. We further highlight some additional use cases for which our vanishing point detection algorithm can be used.

This is a preview of subscription content, log in to check access.

References

  1. 1.
    Almansa, A., Desolneux, A., Vamech, S.: Vanishing point detection without any a priori information. IEEE Trans. Pattern Anal. Mach. Intell. 25(4), 502–507 (2003)CrossRefGoogle Scholar
  2. 2.
    Antunes, M., Barreto, J.P.: A global approach for the detection of vanishing points and mutually orthogonal vanishing directions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1336–1343 (2013)Google Scholar
  3. 3.
    Barinova, O., Lempitsky, V., Tretiak, E., Kohli, P.: Geometric image parsing in man-made environments. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 57–70. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15552-9_5 CrossRefGoogle Scholar
  4. 4.
    Barnard, S.T.: Interpreting perspective images. Artif. Intell. 21(4), 435–462 (1983)CrossRefGoogle Scholar
  5. 5.
    Beardsley, P., Murray, D.: Camera calibration using vanishing points. In: Hogg, D., Boyle, R. (eds.) BMVC92, pp. 416–425. Springer, London (1992)CrossRefGoogle Scholar
  6. 6.
    Borji, A.: Vanishing point detection with convolutional neural networks. arXiv preprint arXiv:1609.00967 (2016)
  7. 7.
    Coughlan, J.M., Yuille, A.L.: Manhattan world: compass direction from a single image by Bayesian inference. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 941–947. IEEE (1999)Google Scholar
  8. 8.
    Criminisi, A., Reid, I., Zisserman, A.: Single view metrology. Int. J. Comput. Vis. 40(2), 123–148 (2000)CrossRefMATHGoogle Scholar
  9. 9.
    Denis, P., Elder, J.H., Estrada, F.J.: Efficient edge-based methods for estimating manhattan frames in urban imagery. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 197–210. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-88688-4_15 CrossRefGoogle Scholar
  10. 10.
    Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Rob. Res. (IJRR) 32, 1231–1237 (2013)CrossRefGoogle Scholar
  11. 11.
    von Gioi, R.G., Jakubowicz, J., Morel, J.M., Randall, G.: LSD: a fast line segment detector with a false detection control. IEEE Trans. Pattern Anal. Mach. Intell. 32(4), 722–732 (2010)CrossRefGoogle Scholar
  12. 12.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)MATHGoogle Scholar
  13. 13.
    Hedau, V., Hoiem, D., Forsyth, D.: Recovering the spatial layout of cluttered rooms. In: 2009 IEEE 12th international conference on Computer Vision, pp. 1849–1856. IEEE (2009)Google Scholar
  14. 14.
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)
  15. 15.
    Košecká, J., Zhang, W.: Video compass. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 476–490. Springer, Heidelberg (2002). doi: 10.1007/3-540-47979-1_32 CrossRefGoogle Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  17. 17.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  18. 18.
    Lezama, J., von Gioi, R.G., Randall, G., Morel, J.M.: Finding vanishing points via point alignments in image primal and dual domains. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 509–515 (2014)Google Scholar
  19. 19.
    Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., Duchesnay, E.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)MathSciNetMATHGoogle Scholar
  20. 20.
    Rother, C.: A new approach to vanishing point detection in architectural environments. Image Vis. Comput. 20(9), 647–655 (2002)CrossRefGoogle Scholar
  21. 21.
    Schindler, G., Dellaert, F.: Atlanta world: an expectation maximization framework for simultaneous low-level edge grouping and camera calibration in complex man-made environments. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2004, vol. 1, p. I. IEEE (2004)Google Scholar
  22. 22.
    Tardif, J.P.: Non-iterative approach for fast and accurate vanishing point detection. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1250–1257. IEEE (2009)Google Scholar
  23. 23.
    Ueda, Y., Kamakura, Y., Saiki, J.: Eye movements converge on vanishing points during visual search. Jpn. Psychol. Res. 59, 109–121 (2017)CrossRefGoogle Scholar
  24. 24.
    Vedaldi, A., Zisserman, A.: Self-similar sketch. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 87–100. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33709-3_7 CrossRefGoogle Scholar
  25. 25.
    Wildenauer, H., Hanbury, A.: Robust camera self-calibration from monocular images of Manhattan worlds. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2831–2838. IEEE (2012)Google Scholar
  26. 26.
    Workman, S., Zhai, M., Jacobs, N.: Horizon lines in the wild. arXiv preprint arXiv:1604.02129 (2016)
  27. 27.
    Xu, Y., Oh, S., Hoogs, A.: A minimum error vanishing point detection approach for uncalibrated monocular images of man-made environments. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1376–1383 (2013)Google Scholar
  28. 28.
    Zhai, M., Workman, S., Jacobs, N.: Detecting vanishing points using global image context in a non-manhattan world. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5657–5665 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Leibniz Universität HannoverHanoverGermany
  2. 2.University of TwenteEnschedeThe Netherlands

Personalised recommendations