Making Affine Correspondences Work in Camera Geometry Computation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F20%3A00342286" target="_blank" >RIV/68407700:21230/20:00342286 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/20:00342286

Result on the web

<a href="https://doi.org/10.1007/978-3-030-58621-8_42" target="_blank" >https://doi.org/10.1007/978-3-030-58621-8_42</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-58621-8_42" target="_blank" >10.1007/978-3-030-58621-8_42</a>

Result language

angličtina

Original language name

Making Affine Correspondences Work in Camera Geometry Computation

Original language description

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.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2020

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computer Vision - ECCV 2020, Part XI

ISBN

978-3-030-58620-1

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

18

Pages from-to

723-740

Publisher name

Springer International Publishing

Place of publication

Cham

Event location

Glasgow

Event date

Aug 23, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—