Advertisement

Visibility Probability Structure from SfM Datasets and Applications

  • Siddharth Choudhary
  • P. J. Narayanan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7576)

Abstract

Large scale reconstructions of camera matrices and point clouds have been created using structure from motion from community photo collections. Such a dataset is rich in information; it represents a sampling of the geometry and appearance of the underlying space. In this paper, we encode the visibility information between and among points and cameras as visibility probabilities. The conditional visibility probability of a set of points on a point (or a set of cameras on a camera) can rank points (or cameras) based on their mutual dependence. We combine the conditional probability with a distance measure to prioritize points for fast guided search for the image localization problem. We define dual problem of feature triangulation as finding the 3D coordinates of a given image feature point. We use conditional visibility probability to quickly identify a subset of cameras in which a feature is visible.

References

  1. 1.
    Fraundorfer, F., Wu, C., Frahm, J.M., Pollefeys, M.: Visual word based location recognition in 3d models using distance augmented weighting. In: 3DPVT (2008)Google Scholar
  2. 2.
    Irschara, A., Zach, C., Frahm, J.M., Bischof, H.: From structure-from-motion point clouds to fast location recognition. In: CVPR (2009)Google Scholar
  3. 3.
    Li, Y., Snavely, N., Huttenlocher, D.P.: Location Recognition Using Prioritized Feature Matching. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 791–804. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2d-to-3d matching. In: ICCV (2011)Google Scholar
  5. 5.
    Goesele, M., Snavely, N., Curless, B., Hoppe, H., Seitz, S.: Multi-view stereo for community photo collections. In: ICCV (2007)Google Scholar
  6. 6.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards internet-scale multi-view stereo. In: CVPR (2010)Google Scholar
  7. 7.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. ACM Trans. Graph. 25, 835–846 (2006)CrossRefGoogle Scholar
  8. 8.
    Agarwal, S., Snavely, N., Simon, I., Seitz, S., Szeliski, R.: Building rome in a day. In: ICCV (2009)Google Scholar
  9. 9.
    Frahm, J.M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: Building Rome on a Cloudless Day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  10. 10.
    Crandall, D., Owens, A., Snavely, N., Huttenlocher, D.P.: Discrete-continuous optimization for large-scale structure from motion. In: CVPR (2011)Google Scholar
  11. 11.
    Robertson, D., Cipolla, R.: An image-based system for urban navigation. In: BMVC (2004)Google Scholar
  12. 12.
    Schindler, G., Brown, M., Szeliski, R.: City-scale location recognition. In: CVPR (2007)Google Scholar
  13. 13.
    Hays, J., Efros, A.A.: im2gps: estimating geographic information from a single image. In: CVPR (2008)Google Scholar
  14. 14.
    Fraundorfer, F., Engels, C., Nistér, D.: Topological mapping, localization and navigation using image collections. In: IROS (2007)Google Scholar
  15. 15.
    Chli, M., Davison, A.J.: Active Matching. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 72–85. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  16. 16.
    Alcantarilla, P.F., Ni, K., Bergasa, L.M., Dellaert, F.: Visibility learning for large-scale urban environment. In: ICRA (2011)Google Scholar
  17. 17.
    Ni, K., Kannan, A., Criminisi, A., Winn, J.: Epitomic location recognition. In: CVPR (2008)Google Scholar
  18. 18.
    Li, X., Wu, C., Zach, C., Lazebnik, S., Frahm, J.M.: Modeling and Recognition of Landmark Image Collections Using Iconic Scene Graphs. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 427–440. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  19. 19.
    Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal sets for efficient structure from motion. In: CVPR (2008)Google Scholar
  20. 20.
    Fuhrmann, S., Goesele, M.: Fusion of depth maps with multiple scales. ACM Trans. Graph. 30, 148:1–148:8 (2011)CrossRefGoogle Scholar
  21. 21.
    Roberts, R., Sinha, S., Szeliski, R., Steedly, D.: Structure from motion for scenes with large duplicate structures. In: CVPR (2011)Google Scholar
  22. 22.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision (2004)Google Scholar
  23. 23.
    Seo, Y., Hartley, R.I.: Sequential L  ∞  Norm Minimization for Triangulation. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds.) ACCV 2007, Part II. LNCS, vol. 4844, pp. 322–331. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Siddharth Choudhary
    • 1
  • P. J. Narayanan
    • 1
  1. 1.Center for Visual Information TechnologyInternational Institute of Information TechnologyHyderabadIndia

Personalised recommendations