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Shape from Angle Regularity

  • Aamer Zaheer
  • Maheen Rashid
  • Sohaib Khan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7577)

Abstract

This paper deals with automatic Single View Reconstruction (SVR) of multi-planar scenes characterized by a profusion of straight lines and mutually orthogonal line-pairs. We provide a new shape-from-X constraint based on this regularity of angles between line-pairs in man-made scenes. First, we show how the presence of such regular angles can be used for 2D rectification of an image of a plane. Further, we propose an automatic SVR method assuming there are enough orthogonal line-pairs available on each plane. This angle regularity is only imposed on physically intersecting line-pairs, making it a local constraint. Unlike earlier literature, our approach does not make restrictive assumptions about the orientation of the planes or the camera and works for both indoor and outdoor scenes. Results are shown on challenging images which would be difficult to reconstruct for existing automatic SVR algorithms.

Keywords

Articulation Line Plane Orientation Relative Depth Outdoor Scene Orientation Label 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Lee, D., Hebert, M., Kanade, T.: Geometric reasoning for single image structure recovery. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 2136–2143. IEEE (2009)Google Scholar
  2. 2.
    Hoiem, D., Efros, A., Hebert, M.: Automatic photo pop-up. ACM Transactions on Graphics (TOG) 24, 577–584 (2005)CrossRefGoogle 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, Part II. LNCS, vol. 6312, pp. 57–70. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Horry, Y., Anjyo, K., Arai, K.: Tour into the picture: using a spidery mesh interface to make animation from a single image. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, pp. 225–232. ACM Press/Addison-Wesley Publishing Co. (1997)Google Scholar
  5. 5.
    Liebowitz, D., Criminisi, A., Zisserman, A.: Creating architectural models from images. Computer Graphics Forum 18, 39–50 (1999)CrossRefGoogle Scholar
  6. 6.
    Sturm, P., Maybank, S.: A method for interactive 3d reconstruction of piecewise planar objects from single images. In: British Machine Vision Conference (1999)Google Scholar
  7. 7.
    Kang, H., Pyo, S., Anjyo, K., Shin, S.: Tour into the picture using a vanishing line and its extension to panoramic images. Computer Graphics Forum 20, 132–141 (2001)CrossRefGoogle Scholar
  8. 8.
    Barinova, O., Konushin, V., Yakubenko, A., Lee, K., Lim, H., Konushin, A.: Fast Automatic Single-View 3-d Reconstruction of Urban Scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 100–113. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  9. 9.
    Hoiem, D., Efros, A., Hebert, M.: Geometric context from a single image. In: Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 654–661. IEEE (2005)Google Scholar
  10. 10.
    Delage, E., Lee, H., Ng, A.: A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image. In: Computer Vision and Pattern Recognition, vol. 2, pp. 2418–2428. IEEE (2006)Google Scholar
  11. 11.
    Saxena, A., Sun, M., Ng, A.: Make3d: learning 3d scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 824–840 (2008)Google Scholar
  12. 12.
    Zhang, Z., Liang, X., Ganesh, A., Ma, Y.: TILT: Transform Invariant Low-Rank Textures. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part III. LNCS, vol. 6494, pp. 314–328. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Kanade, T.: A theory of Origami world. Artificial Intelligence 13, 279–311 (1980)MathSciNetzbMATHCrossRefGoogle Scholar
  14. 14.
    Yu, S., Zhang, H., Malik, J.: Inferring spatial layout from a single image via depth-ordered grouping. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp. 1–7. IEEE (2008)Google Scholar
  15. 15.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004) ISBN: 0521540518 Google Scholar
  16. 16.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  17. 17.
    von Gioi, R., Jakubowicz, J., Morel, J., Randall, G.: LSD: A Fast Line Segment Detector with a False Detection Control. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 722–732 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Aamer Zaheer
    • 1
  • Maheen Rashid
    • 1
  • Sohaib Khan
    • 1
  1. 1.LUMS School of Science and EngineeringLahorePakistan

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