Robust Unambiguous Parametrization of the Essential Manifold.
Raghav Subbarao(1,2), Yakup Genc(2) and Peter Meer(1)
(1)Department of Electrical and Computer Engineering
Rutgers University, Piscataway, NJ 08854, USA
(2)Real Time Vision and Modeling Group
Siemens Corporate Research
Analytic manifolds were recently used for motion averaging,
segmentation and robust estimation. Here we consider
the epipolar constraint for calibrated cameras, which
is the most general motion model for calibrated cameras
and is encoded by the essential matrix. The set of all essential
matrices forms the essential manifold. We provide a
theoretical characterization of the geometry of the essential
manifold and develop a parametrization which associates
each essential matrix with a unique point on the manifold.
Our work provides a more complete theoretical analysis of
the essential manifold than previous work in this direction.
We show the results of using this parametrization with real
data sets, while previous work concentrated on theoretical
analysis with synthetic data.
2008 Computer Vision and Pattern Recognition Conference,
Anchorage, Alaska, June 2008.
Return to Research: Robust Analysis of Visual Data