Fundamental Matrix Estimation Using Relative Depths
Approved
Classifications
MinEdu publication type
A4 Article in conference proceedings (peer-reviewed)
Definition
Article
Target group
Scientific
Peer reviewed
Peer-reviewed
Article type
Other article
Host publication type
Conference platform
Publication channel information
Title of host publication
Computer Vision - ECCV 2024 : 18th European Conference Milan, Italy, September 29–October 4, 2024 Proceedings, Part LXXI
Editors of host publication
Leonardis, A; Ricci, E; Roth, S; Russakovsky, O; Sattler, T; Varol, G
ISBN (print)
978-3-031-73208-9
ISBN (electronic)
978-3-031-73209-6
Name of conference
European conference on computer vision
Title of journal/series
Lecture notes in computer science
ISSN (print)
0302-9743
ISSN (electronic)
1611-3349
ISSN (linking)
0302-9743
Publisher
Springer
Publication forum ID
55638
Publication forum level
2
Country of publication
Switzerland
Internationality
Yes
Detailed publication information
Publication year
2025
Bibliographical publication year
2025
Reporting year
2025
Journal/series issue number
15129
Page numbers
142-159
DOI
10.1007/978-3-031-73209-6_9
Language of publication
English
Co-publication information
International co-publication
Yes
Co-publication with a company
No
Availability
Link to online publication
Classification and additional information
MinEdu field of science classification
213 Electronic, automation and communications engineering, electronics
Keywords
Fundamental Matrix; Relative Depth
Funding information
Funding information in the publication
This work was supported by the Czech Science Foundation (GACR) JUNIOR STAR Grant No. 22-23183M, and the National Natural Science Foundation of China under Grant No. 62361166670.
Source database ID
WoS ID
WOS:001353702500009
Scopus ID
2-s2.0-85210040703