Scene Reconstruction from Multiple Uncalibrated Views

We describe a factorization-based method to reconstruct Euclidean shape and motion from multiple perspective views with uncalibrated cameras. The method rst performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. We present three factorization-based normalization algorithms to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews. The rst two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear, dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. Experimental results show that out method is e cient and reliable. @2000 Carnegie Mellon University

[1]  Takeo Kanade,et al.  A Paraperspective Factorization Method for Shape and Motion Recovery , 1994, ECCV.

[2]  Olivier D. Faugeras,et al.  What can be seen in three dimensions with an uncalibrated stereo rig , 1992, ECCV.

[3]  Anders Heyden,et al.  Euclidean reconstruction from image sequences with varying and unknown focal length and principal point , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Richard I. Hartley,et al.  Euclidean Reconstruction from Uncalibrated Views , 1993, Applications of Invariance in Computer Vision.

[5]  Richard I. Hartley,et al.  Linear self-calibration of a rotating and zooming camera , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[6]  Reinhard Koch,et al.  Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[7]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[8]  Bill Triggs,et al.  Factorization methods for projective structure and motion , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  Bill Triggs,et al.  Autocalibration and the absolute quadric , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Anders Heyden.  Reduced Multilinear Constraints: Theory and Experiments , 2004, International Journal of Computer Vision.

[11]  Anders Heyden.  Projective Structure and Motion from Image Sequences using Subspace Methods , 1997 .

[12]  Bill Triggs,et al.  Matching constraints and the joint image , 1995, Proceedings of IEEE International Conference on Computer Vision.

[13]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[14]  Stéphane Christy,et al.  Euclidean Shape and Motion from Multiple Perspective Views by Affine Iterations , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Ian D. Reid,et al.  Recursive Affine Structure and Motion from Image Sequences , 1994, ECCV.

[16]  Alex Pentland,et al.  Recursive Estimation of Motion, Structure, and Focal Length , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Long Quan,et al.  Relative 3D Reconstruction Using Multiple Uncalibrated Images , 1995, Int. J. Robotics Res..

[18]  Paul A. Beardsley,et al.  3D Model Acquisition from Extended Image Sequences , 1996, ECCV.

[19]  Stéphane Christy,et al.  Euclidean Reconstruction: From Paraperspective to Perspective , 1996, ECCV.