Hippi: Higher-order projected power iterations for scalable multi-matching
(2019) 2019 IEEE/CVF International Conference on Computer Vision (ICCV) p.10284-10293- Abstract
- The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric consistency between points into account. Computationally, the multi-matching problem is difficult. It can be phrased as simultaneously solving multiple (NP-hard) quadratic assignment problems (QAPs) that are coupled via cycle-consistency constraints. The main limitations of existing multi-matching methods are that they either ignore geometric consistency and thus have limited robustness, or they are restricted to small-scale problems due to their (relatively) high computational cost. We address... (More)
- The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric consistency between points into account. Computationally, the multi-matching problem is difficult. It can be phrased as simultaneously solving multiple (NP-hard) quadratic assignment problems (QAPs) that are coupled via cycle-consistency constraints. The main limitations of existing multi-matching methods are that they either ignore geometric consistency and thus have limited robustness, or they are restricted to small-scale problems due to their (relatively) high computational cost. We address these shortcomings by introducing a Higher-order Projected Power Iteration method, which is (i) efficient and scales to tens of thousands of points, (ii) straightforward to implement, (iii) able to incorporate geometric consistency, (iv) guarantees cycle-consistent multi-matchings, and (iv) comes with theoretical convergence guarantees. Experimentally we show that our approach is superior to existing methods. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7786b3c7-b9c9-4499-859c-2d53d1241843
- author
- Bernard, Florian ; Thunberg, Johan LU ; Swoboda, Paul and Theobalt, Christian
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- pages
- 10 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 2019 IEEE/CVF International Conference on Computer Vision (ICCV)
- conference location
- Seoul, Korea, Republic of
- conference dates
- 2019-10-27 - 2019-11-02
- external identifiers
-
- scopus:85081928419
- ISBN
- 978-1-7281-4803-8
- DOI
- 10.1109/ICCV.2019.01038
- language
- English
- LU publication?
- no
- id
- 7786b3c7-b9c9-4499-859c-2d53d1241843
- date added to LUP
- 2024-09-05 14:00:28
- date last changed
- 2024-09-16 18:00:25
@inproceedings{7786b3c7-b9c9-4499-859c-2d53d1241843, abstract = {{The matching of multiple objects (e.g. shapes or images) is a fundamental problem in vision and graphics. In order to robustly handle ambiguities, noise and repetitive patterns in challenging real-world settings, it is essential to take geometric consistency between points into account. Computationally, the multi-matching problem is difficult. It can be phrased as simultaneously solving multiple (NP-hard) quadratic assignment problems (QAPs) that are coupled via cycle-consistency constraints. The main limitations of existing multi-matching methods are that they either ignore geometric consistency and thus have limited robustness, or they are restricted to small-scale problems due to their (relatively) high computational cost. We address these shortcomings by introducing a Higher-order Projected Power Iteration method, which is (i) efficient and scales to tens of thousands of points, (ii) straightforward to implement, (iii) able to incorporate geometric consistency, (iv) guarantees cycle-consistent multi-matchings, and (iv) comes with theoretical convergence guarantees. Experimentally we show that our approach is superior to existing methods.}}, author = {{Bernard, Florian and Thunberg, Johan and Swoboda, Paul and Theobalt, Christian}}, booktitle = {{2019 IEEE/CVF International Conference on Computer Vision (ICCV)}}, isbn = {{978-1-7281-4803-8}}, language = {{eng}}, pages = {{10284--10293}}, publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}}, title = {{Hippi: Higher-order projected power iterations for scalable multi-matching}}, url = {{http://dx.doi.org/10.1109/ICCV.2019.01038}}, doi = {{10.1109/ICCV.2019.01038}}, year = {{2019}}, }