International Journal of Computer Vision

, Volume 68, Issue 1, pp 7–25

Two-View Multibody Structure from Motion

Authors

    • Center for Imaging Science, Department of Biomedical EngineeringJohns Hopkins University
  • Yi Ma
    • Department of ECEUniversity of Illinois at Urbana-Champaign
  • Stefano Soatto
    • Computer Science DepartmentUniversity of California at Los Angeles
  • Shankar Sastry
    • Department of EECSUniversity of California at Berkeley
Article

DOI: 10.1007/s11263-005-4839-7

Cite this article as:
Vidal, R., Ma, Y., Soatto, S. et al. Int J Comput Vision (2006) 68: 7. doi:10.1007/s11263-005-4839-7
  • 213 Views

Abstract

We present an algebraic geometric approach to 3-D motion estimation and segmentation of multiple rigid-body motions from noise-free point correspondences in two perspective views. Our approach exploits the algebraic and geometric properties of the so-called multibody epipolar constraint and its associated multibody fundamental matrix, which are natural generalizations of the epipolar constraint and of the fundamental matrix to multiple motions. We derive a rank constraint on a polynomial embedding of the correspondences, from which one can estimate the number of independent motions as well as linearly solve for the multibody fundamental matrix. We then show how to compute the epipolar lines from the first-order derivatives of the multibody epipolar constraint and the epipoles by solving a plane clustering problem using Generalized PCA (GPCA). Given the epipoles and epipolar lines, the estimation of individual fundamental matrices becomes a linear problem. The clustering of the feature points is then automatically obtained from either the epipoles and epipolar lines or from the individual fundamental matrices. Although our approach is mostly designed for noise-free correspondences, we also test its performance on synthetic and real data with moderate levels of noise.

Keywords

multibody structure from motion3-D motion segmentationmultibody epipolar constraintmultibody fundamental matrixGeneralized PCA (GPCA)
Download to read the full article text

Copyright information

© Springer Science + Business Media, LLC. 2006