The paper „Learning to Solve Hard Minimal Problems“ by co-authors Petr Hrubý (ETH Zurich), Timothy Duff (University of Washington), Anton Leykin (Georgia Institute of Technology) and Tomáš Pajdla (CIIRC CTU) received the Best Paper Award at the CVPR 2022 conference.
The IEEE/CVF CVPR 2022 (Conference on Computer Vision and Pattern Recognition), which took place at the end of June in New Orleans, ranks among first-class scientific events. The overall acceptance rate for submitted papers is 25 percent, and the prize-winning paper „Learning to Solve Hard Minimal Problems“ was selected as the best by a renowned scientific committee among more than 8,000 submitted papers.
Tomáš Pajdla, who works at the Czech Institute of Informatics, Robotics and Cybernetics (CIIRC) of the Czech Technical University, is the fourth best Czech computer scientist according to the Research.com Ranking.
The winning contribution by co-authors Petr Hrubý, Timothy Duff, Anton Leykin and Tomáš Pajdla presents an approach to solving hard geometric optimization problems within RANSAC. The main idea is to use machine learning methods to speed up the calculation of solutions to systems of polynomial equations and its effective implementation in the field of minimal problems in computer vision. We find computer vision in applications everywhere around us, where it is necessary to recognize the three-dimensional world using an image, i.e. in online maps, smartphones or autonomous driving.
See https://www.ciirc.cvut.cz/cvpr2022/ for more details.
