Fundamental matrix estimation using relative depths

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00380046" target="_blank" >RIV/68407700:21230/25:00380046 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-73209-6_9" target="_blank" >https://doi.org/10.1007/978-3-031-73209-6_9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-73209-6_9" target="_blank" >10.1007/978-3-031-73209-6_9</a>

Result language

angličtina

Original language name

Fundamental matrix estimation using relative depths

Original language description

We propose a novel approach to estimate the fundamental matrix from point correspondences and their relative depths. Relative depths can be approximated from the scales of local features, which are commonly available or can be obtained from non-metric monocular depth estimates provided by popular deep learning-based methods. This makes the considered problem very relevant. To derive efficient solutions, we explore new geometric constraints on the fundamental matrix with known relative depths and present new algebraic constraints between the fundamental matrix and the translation vector. Using point correspondences and their relative depths, we derive novel efficient minimal solvers for two fully uncalibrated cameras, two cameras with different unknown focal lengths, and two cameras with equal unknown focal lengths, respectively. We propose different variants of these solvers based on the source of the relative depth information. We present detailed analyses and comparisons with state-of-the-art solvers, including results with 86, 306 image pairs from three large-scale datasets

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

<a href="/en/project/GM22-23183M" target="_blank" >GM22-23183M: New generation of camera geometry solvers</a><br>

Continuities

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

Publication year

2025

Confidentiality

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

Article name in the collection

Computer Vision – ECCV 2024, Part LXXI

ISBN

978-3-031-73208-9

ISSN

0302-9743

e-ISSN

1611-3349

Number of pages

18

Pages from-to

142-159

Publisher name

Springer, Cham

Place of publication

—

Event location

Milano

Event date

Sep 29, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

001353702500009