Abstract
This paper presents two methods for automatic 3D reconstruction: the one is a quantitative measure for frame grouping over long un-calibrated sequences, and the other is 3D reconstruction algorithm based on projective invariance. The first method evaluates the duration of corresponding points over sequence, the homography error, and the distribution of correspondences in the image. By making efficient bundles, we can overcome the limitation of the factorization, which is the assumption that all correspondences must remain in all views. In addition, we use collinearity among invariant properties in projective space to refine the projective matrix. That means any points located on the 2D imaged line must lie on the reconstructed projective line. Therefore, we regard the points unsatisfying collinearity as outliers, which are caused by a false feature tracking. After fitting a new 3D line from projective points, we iteratively obtain more precise projective matrix by using the points that are the orthogonal projection of outliers onto the line. Experimental results showed our methods can recover efficiently 3D structure from un-calibrated sequences.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Fitzgibbon, A.W., Zisserman, A.: Automatic camera recovery for closed or open image sequences. In: Proc. of 6th European Conference on Computer Vision, June 3, pp. 311–326 (1998)
Cornelis, K., Pollefeys, M., Vergauwen, M., Gool, L.V.: Augmented reality from uncalibrated video sequences. In: Pollefeys, M., Van Gool, L., Zisserman, A., Fitzgibbon, A.W. (eds.) SMILE 2000. LNCS, vol. 2018, pp. 144–160. Springer, Heidelberg (2001)
Gibson, S., Cook, J., Howard, T., Hubbold, R., Oram, D.: Accurate camera calibration for off-line, video-based augmented reality. In: Proc. IEEE and ACM ISMAR, Darmstadt, Germany (September 2002)
Chiuso, A., Favaro, P., Jin, H., Soatto, S.: Motion and structure causally integrated over time. IEEE Trans. on Pattern Matching and Machine Intelligence 24(4), 523–535 (2002)
Sturm, P., Triggs, B.: A factorization based algorithm for multi-image projective structure and motion. In: Proc. of 4th European Conference on Computer Vision, Cambridge, England, April 1996, pp. 709–720 (1996)
Hartley, R., Zisserman, A.: Mutiple View Geometry in Computer Vision. Cambrige Univ. Press, Cambridge (2000)
Zhang, Z.: Computing rectifying homographies for stereo vision. In: Proc. of IEEE CVPR, Fort Collins Colorado, June 1999, pp. 125–131 (1999)
Intille, S.S., Bobick, A.F.: Disparity-space images and large occlusion stereo. In: 2nd European Conference on Computer Vision, Stockholm, Sweden, pp. 179–186 (1994)
Kanade, D., Okutomi, M.: A stereo matching algorithm with an adaptive window: theory and experiment. IEEE Transactions of Pattern Anal. Machine Intell. 16(9), 920–932 (1994)
Debecec, P., Taylor, C., Malik, J.: Modeling and rendering architecture from photos: a hybrid geometry and image-base approach. In: In Proc. of SIGGRAPH 1996, pp. 11–20 (1996)
Gibson, S., Hubbold, R.J., Cook, J., Howard, T.: Interactive reconstruction of virtual environments from video sequences. Computer Graphics 27, 293–301 (2003)
Sato, T., Kanbara, M., Yokoya, N., Takemura, H.: Dense 3-D reconstruction of outdoor scene by hundreds-baseline stereo using a hand-held video camera. In: Proc. of IEEE Workshop on Stereo and Multi-baseline vision (2001)
Heyden, A., Berthilsson, R., Sparr, G.: An iterative factorization method for projective structure and motion from image sequences. Image and Vision Computing, 981-991 (1999)
Hartley, R.: Defence of the 8-Point algorithm. In: Proc. IEEE ICCV, pp. 1064–1070 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jeong, YY., Seok, BR., Hwang, YH., Hong, HK. (2004). Two Methods for Automatic 3D Reconstruction from Long Un-calibrated Sequences. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_55
Download citation
DOI: https://doi.org/10.1007/978-3-540-28651-6_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
eBook Packages: Springer Book Archive