Learning to Solve Hard Minimal Problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F22%3A00364413" target="_blank" >RIV/68407700:21730/22:00364413 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/CVPR52688.2022.00545" target="_blank" >https://doi.org/10.1109/CVPR52688.2022.00545</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/CVPR52688.2022.00545" target="_blank" >10.1109/CVPR52688.2022.00545</a>
Alternative languages
Result language
angličtina
Original language name
Learning to Solve Hard Minimal Problems
Original language description
We present an approach to solving hard geometric optimization problems in the RANSAC framework. The hard minimal problems arise from relaxing the original geometric optimization problem into a minimal problem with many spurious solutions. Our approach avoids computing large numbers of spurious solutions. We design a learning strategy for selecting a starting problem-solution pair that can be numerically continued to the problem and the solution of interest. We demonstrate our approach by developing a RANSAC solver for the problem of computing the relative pose of three calibrated cameras, via a minimal relaxation using four points in each view. On average, we can solve a single problem in under 70 mu s. We also benchmark and study our engineering choices on the very familiar problem of computing the relative pose of two calibrated cameras, via the minimal case of five points in two views.
Czech name
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Czech description
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Classification
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)
Result continuities
Project
<a href="/en/project/EF15_003%2F0000468" target="_blank" >EF15_003/0000468: Intelligent Machine Perception</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceeding 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
ISBN
978-1-6654-6946-3
ISSN
1063-6919
e-ISSN
2575-7075
Number of pages
11
Pages from-to
5522-5532
Publisher name
IEEE
Place of publication
Piscataway
Event location
New Orleans, Louisiana
Event date
Jun 19, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000867754205076