SOREPP
Yehonatan Goldman, Ehud Rivlin, and Ilan Shimshoni
Abstract
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 such as smartphones produce geotagged
images containing pose and internal calibration data.
These include a GPS receiver, which estimates the position,
a compass, accelerometers, and gyros, which estimate the orientation, and the
focal length. 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 (Soft Optimization method for Robust
Estimation based on Pose Priors), 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
fraction. SOREPP was extensively tested on synthetic data and on hundreds of
real image pairs taken by smartphones. Its ability to handle challenging
scenarios with extremely low inlier fractions of less than 10% was
demonstrated. It outperforms current stateoftheart algorithms that do not
use pose priors as well as others that do.
The paper
Y. Goldman, E. Rivlin, and I. Shimshoni, “Robust Epipolar Geometry Estimation Using Noisy Pose Priors”. In Image and vision computing, Vol 67, November 2017, pages 1628.
Implementation
A demo, including Matlab mex and the SOREPP's dll are available here. The code is compiled for Windows 64 bit. The demo includes also a description of the interface, a dll which reads the pose data from jpeg files of images taken by SitisMobile's GeoCam application, and instruction how to apply the demo on new images.
The code of SOREPP and a readme file describing how to compile it are available here.
The full datasets used for the paper and some Matlab functions which runs SOREPP on it are available here.
If you have any questions please refer to Yehonatan Goldman yehonatan.goldman@gmail.com or Ilan Shimshoni ishimshoni@is.haifa.ac.il .
Examples
Some results of SOREPP on challenging image pairs are given in the following table. The left images are the reference images with some control points marked, and the right images are the target images with control points marked, as well as the corresponding epipolar lines based on the estimation result of SOREPP.























