Invited Talks - Pattern Recognition Lab

A Sparse Resultant Based Method for Efficient Minimal Solvers

Presenter: Snehal Bhayani – University of Oulu (Center for Machine Vision and Signal Analysis)

Short Abstract: The idea is to model geometry problems by using minimum amount of information. Many computer algebra software can be used to solve such problems accurately and efficiently. Solving such problems is usually divided into two stages, Offline and Online. Offline stage involves the more time consuming operations that are executed once per type of problem, e.g. relative pose (stereo) for calibrated cameras can be solved from 5-point correspondences. Presented approach compares against the S.O.T.A. in terms of speed and accuracy. The underlying tools are inspired from algebraic geometry.

Published at CVPR 2020 [bibtex]:

Bhayani, Snehal, Zuzana Kukelova, and Janne Heikkila. “A sparse resultant based method for efficient minimal solvers.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1770-1779. 2020.