Yehonatan Goldman, M.Sc. Thesis Seminar
Wednesday, 29.5.2013, 14:30
Epipolar geometry estimation is fundamental to many
computer vision algorithms. It has therefore attracted a lot of
interest in recent years, yielding high quality estimation
algorithms for wide baseline image pairs. Currently many types of
cameras (e.g., in smartphones and robot navigation systems) produce
geo-tagged images containing pose and internal calibration data.
Exploiting this information as part of an epipolar geometry estimation
algorithm may be useful but not trivial, since the pose measurement may be quite noisy.
We introduce SOREPP, a novel estimation algorithm designed
to exploit pose priors naturally. It sparsely samples the pose
space around the measured pose and for a few promising candidates
applies a robust optimization procedure. It uses all the putative
correspondences simultaneously, even though many of them are
outliers, yielding a very efficient algorithm whose runtime
is independent of the inlier fractions.
SOREPP was extensively tested on synthetic data and on hundreds
of real image pairs taken by a smartphone. Its ability to handle
challenging scenarios with extremely low inlier fractions of
less than 10% was demonstrated as was its ability to handle close cameras.
It outperforms current state-of-the-art algorithms that do not use pose
priors as well as other algorithms that do.