Robust Fitting for Multiple View Geometry

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

How hard are geometric vision problems with outliers? We show that for most fitting problems, a solution that minimizes the num- ber of outliers can be found with an algorithm that has polynomial time- complexity in the number of points (independent of the rate of outliers). Further, and perhaps more interestingly, other cost functions such as the truncated L2 -norm can also be handled within the same framework with the same time complexity. We apply our framework to triangulation, relative pose problems and stitching, and give several other examples that fulfill the required condi- tions. Based on efficient polynomial equation solvers, it is experimentally demonstrated that these problems can be solved reliably, in particular for low-dimensional models. Comparisons to standard random sampling solvers are also given.

Details

Authors
Organisations
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Mathematics

Keywords

  • geometry, optimization, computer vision
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (Computer Vision - ECCV 2012, Proceedings of the 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Part I )
EditorsAndrew Fitzgibbon, Svetlana Lazebnik, Pietro Perona, Yoichi Sato, Cordelia Schmid
PublisherSpringer
Pages738-751
Number of pages14
Volume7572
ISBN (Print)978-3-642-33717-8 (print), 978-3-642-33718-5 (online)
Publication statusPublished - 2012
Publication categoryResearch
Peer-reviewedYes
Event12th European Conference on Computer Vision (ECCV 2012) - Florence, Italy
Duration: 2012 Oct 72012 Oct 13

Publication series

Name
Volume7572
ISSN (Print)1611-3349
ISSN (Electronic)0302-9743

Conference

Conference12th European Conference on Computer Vision (ECCV 2012)
CountryItaly
CityFlorence
Period2012/10/072012/10/13