Global Motion Estimation from Point Matches

• Published in: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization & Transmission pp. 81 - 88
• Main Authors: Arie-Nachimson, M., Kovalsky, S. Z., Kemelmacher-Shlizerman, I., Singer, A., Basri, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2012
• Subjects: 3D reconstruction; camera motion estimation; Cameras; convex relaxation; Eigenvalues and eigenfunctions; Equations; Estimation; linear estimation; Linear systems; Matrix decomposition; Optimization; structure from motion
• ISBN: 1467344702; 9781467344708
• ISSN: 1550-6185
• DOI: 10.1109/3DIMPVT.2012.46

Multiview structure recovery from a collection of images requires the recovery of the positions and orientations of the cameras relative to a global coordinate system. Our approach recovers camera motion as a sequence of two global optimizations. First, pair wise Essential Matrices are used to recover the global rotations by applying robust optimization using either spectral or semi definite programming relaxations. Then, we directly employ feature correspondences across images to recover the global translation vectors using a linear algorithm based on a novel decomposition of the Essential Matrix. Our method is efficient and, as demonstrated in our experiments, achieves highly accurate results on collections of real images for which ground truth measurements are available.