Degeneracies in Rolling Shutter SfM

  • Cenek Albl
  • Akihiro SugimotoEmail author
  • Tomas Pajdla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9909)


We address the problem of Structure from Motion (SfM) with rolling shutter cameras. We first show that many common camera configurations, e.g. cameras with parallel readout directions, become critical and allow for a large class of ambiguities in multi-view reconstruction. We provide mathematical analysis for one, two and some multi-view cases and verify it by synthetic experiments. Next, we demonstrate that bundle adjustment with rolling shutter cameras, which are close to critical configurations, may still produce drastically deformed reconstructions. Finally, we provide practical recipes how to photograph with rolling shutter cameras to avoid scene deformations in SfM. We evaluate the recipes and provide a quantitative analysis of their performance in real experiments. Our results show how to reconstruct correct 3D models with rolling shutter cameras.


Structure from motion Rolling shutter Degeneracy Non-perspective cameras 



This research was in part supported by Czech Ministry of Education under Project RVO13000, by Grant Agency of the CTU Prague project SGS16/230/OHK3/3T/13 and by Grant-in-Aid for Scientific Research of the Ministry of Education, Culture, Sports, Science and Technology of Japan.


  1. 1.
    Ait-Aider, O., Berry, F.: Structure and kinematics triangulation with a rolling shutter stereo rig. In: IEEE 12th International Conference on Computer Vision, pp. 1835–1840, September 2009Google Scholar
  2. 2.
    Ait-aider, O., Andreff, N., Lavest, J.M., Blaise, U., Ferr, P.C., Cnrs, L.U.: Simultaneous object pose and velocity computation using a single view from a rolling shutter camera. In: Proceedings of the European Conference on Computer Vision, pp. 56–68 (2006)Google Scholar
  3. 3.
    Albl, C., Kukelova, Z., Pajdla, T.: R6p - rolling shutter absolute pose problem. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2292–2300, June 2015Google Scholar
  4. 4.
    Hartley, R., Kahl, F.: Critical configurations for projective reconstruction from multiple views. IJCV 71, 5–47 (2006)CrossRefGoogle Scholar
  5. 5.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, New York (2003)zbMATHGoogle Scholar
  6. 6.
    Hedborg, J., Ringaby, E., Forssen, P.E., Felsberg, M.: Structure and motion estimation from rolling shutter video. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 17–23 (2011)Google Scholar
  7. 7.
    Hedborg, J., Forssèn, P.E., Felsberg, M., Ringaby, E.: Rolling shutter bundle adjustment. In: CVPR, pp. 1434–1441 (2012)Google Scholar
  8. 8.
    Kahl, F., Hartley, R.: Critical curves and surfaces for euclidean reconstruction. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2351, pp. 447–462. Springer, Heidelberg (2002). doi: 10.1007/3-540-47967-8_30 CrossRefGoogle Scholar
  9. 9.
    Klein, G., Murray, D.: Parallel tracking and mapping on a camera phone. In: 8th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2009, pp. 83–86, October 2009Google Scholar
  10. 10.
    Magerand, L., Bartoli, A., Ait-Aider, O., Pizarro, D.: Global optimization of object pose and motion from a single rolling shutter image with automatic 2D-3D matching. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7572, pp. 456–469. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33718-5_33 Google Scholar
  11. 11.
    Meingast, M., Geyer, C., Sastry, S.: Geometric models of rolling-shutter cameras. Comput. Res. Repository (2005)Google Scholar
  12. 12.
    Moulon, P., Monasse, P., Marlet, R.: Global fusion of relative motions for robust, accurate and scalable structure from motion. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 3248–3255, December 2013Google Scholar
  13. 13.
    Oth, L., Furgale, P., Kneip, L., Siegwart, R.: Rolling shutter camera calibration. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1360–1367, June 2013Google Scholar
  14. 14.
    Saurer, O., Koser, K., Bouguet, J.Y., Pollefeys, M.: Rolling shutter stereo. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 465–472, December 2013Google Scholar
  15. 15.
    Saurer, O., Pollefeys, M., Lee, G.H.: A minimal solution to the rolling shutter pose estimation problem. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1328–1334, September 2015Google Scholar
  16. 16.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. In: ACM SIGGRAPH 2006 Papers, pp. 835–846. ACM, New York (2006)Google Scholar
  17. 17.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment — a modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) IWVA 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000). doi: 10.1007/3-540-44480-7_21 CrossRefGoogle Scholar
  18. 18.
    Wu, C.: VisualSFM: A Visual Structure from Motion System (2011)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Czech Technical University in PraguePragueCzech Republic
  2. 2.National Institute of InformaticsTokyoJapan

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