Finding Geometric Models by Clustering in the Consensus Space
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00371780" target="_blank" >RIV/68407700:21230/23:00371780 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR52729.2023.00524" target="_blank" >https://doi.org/10.1109/CVPR52729.2023.00524</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52729.2023.00524" target="_blank" >10.1109/CVPR52729.2023.00524</a>
Alternative languages
Result language
angličtina
Original language name
Finding Geometric Models by Clustering in the Consensus Space
Original language description
We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a model quality function considering previously proposed instances. New ones are found by clustering in the consensus space. This new formulation leads to a simple iterative algorithm with state-of-the-art accuracy while running in real-time on a number of vision problems - at least two orders of magnitude faster than the competitors on two-view motion estimation. Also, we propose a deterministic sampler reflecting the fact that real-world data tend to form spatially coherent structures. The sampler returns connected components in a progressively densified neighborhood-graph. We present a number of applications where the use of multiple geometric models improves accuracy. These include pose estimation from multiple generalized homographies; trajectory estimation of fast-moving objects; and we also propose a way of using multiple homographies in global SfM algorithms. Source code: https://github.com/danini/clustering-in-consensus-space.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
979-8-3503-0129-8
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
11
Pages from-to
5414-5424
Publisher name
IEEE Computer Society
Place of publication
USA
Event location
Vancouver
Event date
Jun 18, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001058542605072