Making Affine Correspondences Work in Camera Geometry Computation
Paper in proceedings, 2020

Local features e.g. SIFT and its affine and learned variants provide region-to-region rather than point-to-point correspondences. This has recently been exploited to create new minimal solvers for classical problems such as homography, essential and fundamental matrix estimation. The main advantage of such solvers is that their sample size is smaller, e.g., only two instead of four matches are required to estimate a homography. Works proposing such solvers often claim a significant improvement in run-time thanks to fewer RANSAC iterations. We show that this argument is not valid in practice if the solvers are used naively. To overcome this, we propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. We propose a method for refining the local feature geometries by symmetric intensity-based matching, combine uncertainty propagation inside RANSAC with preemptive model verification, show a general scheme for computing uncertainty of minimal solvers results, and adapt the sample cheirality check for homography estimation. Our experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times when following our guidelines. We make code available at https://github.com/danini/affine-correspondences-for-camera-geometry.

Author

Daniel Barath

Czech Technical University in Prague

Hungarian Academy of Sciences

Michal Polic

Czech Technical University in Prague

Wolfgang Förstner

University of Bonn

Torsten Sattler

Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering, Imaging and Image Analysis

Czech Technical University in Prague

Tomas Pajdla

Czech Technical University in Prague

Zuzana Kukelova

Czech Technical University in Prague

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 12356 LNCS 723-740

16th European Conference on Computer Vision, ECCV 2020
Glasgow, United Kingdom,

Subject Categories

Computational Mathematics

Control Engineering

Computer Vision and Robotics (Autonomous Systems)

DOI

10.1007/978-3-030-58621-8_42

More information

Latest update

1/5/2021 8