Sequential Reconstruction Segment-Wise Feature Track and Structure Updating Based on Parallax Paths

  • Mauricio Hess-Flores
  • Mark A. Duchaineau
  • Kenneth I. Joy
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7726)


This paper presents a novel method for multi-view sequential scene reconstruction scenarios such as in aerial video, that exploits the constraints imposed by the path of a moving camera to allow for a new way of detecting and correcting inaccuracies in the feature tracking and structure computation processes. The main contribution of this paper is to show that for short, planar segments of a continuous camera trajectory, parallax movement corresponding to a viewed scene point should ideally form a scaled and translated version of this trajectory when projected onto a parallel plane. This creates two constraints, which differ from those of standard factorization, that allow for the detection and correction of inaccurate feature tracks and to improve scene structure. Results are shown for real and synthetic aerial video and turntable sequences, where the proposed method was shown to correct outlier tracks, detect and correct tracking drift, and allow for a novel improvement of scene structure, additionally resulting in an improved convergence for bundle adjustment optimization.


Feature Track Parallax Movement Bundle Adjustment Locus Line Epipolar Line 
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.


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  1. 1.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3d. In: ACM SIGGRAPH 2006, pp. 835–846. ACM, New York (2006)CrossRefGoogle Scholar
  2. 2.
    Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal on Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  3. 3.
    Fischler, M., Bolles, R.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. In: Readings in Computer Vision: Issues, Problems, Principles, and Paradigms, pp. 726–740 (1987)Google Scholar
  4. 4.
    Lourakis, M., Argyros, A.: The design and implementation of a generic sparse bundle adjustment software package based on the Levenberg-Marquardt algorithm. Technical Report 340, Institute of Computer Science - FORTH, Heraklion, Crete, Greece (2000)Google Scholar
  5. 5.
    Tomasi, C., Kanade, T.: Shape and motion from image streams under orthography: A factorization method. IJCV 9, 137–154 (1992)CrossRefGoogle Scholar
  6. 6.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press (2004)Google Scholar
  7. 7.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47, 7–42 (2002)zbMATHCrossRefGoogle Scholar
  8. 8.
    Rodehorst, V., Heinrichs, M., Hellwich, O.: Evaluation of relative pose estimation methods for multi-camera setups. In: ISPRS 2008, Beijing, China, pp. 135–140 (2008)Google Scholar
  9. 9.
    Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR 2006, pp. 519–528. IEEE Computer Society, Washington, DC (2006)Google Scholar
  10. 10.
    Strecha, C., von Hansen, W., Gool, L.J.V., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: CVPR 2008 (2008)Google Scholar
  11. 11.
    Pollefeys, M., Van Gool, L., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. International Journal of Computer Vision 59, 207–232 (2004)CrossRefGoogle Scholar
  12. 12.
    Nistér, D.: Reconstruction from Uncalibrated Sequences with a Hierarchy of Trifocal Tensors. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 649–663. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  13. 13.
    Fitzgibbon, A.W., Cross, G., Zisserman, A.: Automatic 3D Model Construction for Turn-Table Sequences. In: Koch, R., Van Gool, L. (eds.) SMILE 1998. LNCS, vol. 1506, pp. 155–170. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  14. 14.
    Oxford Visual Geometry Group: Multi-view and Oxford Colleges building reconstruction (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mauricio Hess-Flores
    • 1
  • Mark A. Duchaineau
    • 2
  • Kenneth I. Joy
    • 1
  1. 1.Institute for Data Analysis and VisualizationUniversity of CaliforniaDavisUSA
  2. 2.Lawrence Livermore National LaboratoryLivermoreUSA

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