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.
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References
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)
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)
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
Barnard, S.T.: Interpreting perspective images. Artif. Intell. 21(4), 435–462 (1983)
Beardsley, P., Murray, D.: Camera calibration using vanishing points. In: Hogg, D., Boyle, R. (eds.) BMVC92, pp. 416–425. Springer, London (1992)
Borji, A.: Vanishing point detection with convolutional neural networks. arXiv preprint arXiv:1609.00967 (2016)
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)
Criminisi, A., Reid, I., Zisserman, A.: Single view metrology. Int. J. Comput. Vis. 40(2), 123–148 (2000)
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
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Rob. Res. (IJRR) 32, 1231–1237 (2013)
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)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2003)
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)
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)
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
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)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
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)
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)
Rother, C.: A new approach to vanishing point detection in architectural environments. Image Vis. Comput. 20(9), 647–655 (2002)
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)
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)
Ueda, Y., Kamakura, Y., Saiki, J.: Eye movements converge on vanishing points during visual search. Jpn. Psychol. Res. 59, 109–121 (2017)
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
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)
Workman, S., Zhai, M., Jacobs, N.: Horizon lines in the wild. arXiv preprint arXiv:1604.02129 (2016)
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)
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)
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Kluger, F., Ackermann, H., Yang, M.Y., Rosenhahn, B. (2017). Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection. In: Roth, V., Vetter, T. (eds) Pattern Recognition. GCPR 2017. Lecture Notes in Computer Science(), vol 10496. Springer, Cham. https://doi.org/10.1007/978-3-319-66709-6_2
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DOI: https://doi.org/10.1007/978-3-319-66709-6_2
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