Global Trifocal Adjustment
(2019) 21st Scandinavian Conference on Image Analysis, SCIA 2019 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11482 LNCS. p.287-298- Abstract
In this paper we introduce a fast and robust structure-less alternative to full bundle adjustment. The method is based on optimizing algebraic errors for trilinear constraints from triplets of views. It is shown that the error generated by a triplet of views can be described by a fixed triangular matrix regardless of the number of feature correspondences between the views. The method has been evaluated on various real and synthetic datasets and shows good convergence properties with a large convergence basin and solutions that are close to the optimal solution. The method has been compared to Global Epipolar Adjustment, GEA, which is based on the bilinear constraint. It will be shown that the method can handle the degenerate... (More)
In this paper we introduce a fast and robust structure-less alternative to full bundle adjustment. The method is based on optimizing algebraic errors for trilinear constraints from triplets of views. It is shown that the error generated by a triplet of views can be described by a fixed triangular matrix regardless of the number of feature correspondences between the views. The method has been evaluated on various real and synthetic datasets and shows good convergence properties with a large convergence basin and solutions that are close to the optimal solution. The method has been compared to Global Epipolar Adjustment, GEA, which is based on the bilinear constraint. It will be shown that the method can handle the degenerate configurations of GEA.
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- author
- Persson, Patrik LU and Åström, Kalle LU
- organization
- publishing date
- 2019
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- Optimization, SFM, Structure-less bundle adjustment
- host publication
- Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Felsberg, Michael ; Forssén, Per-Erik ; Unger, Jonas and Sintorn, Ida-Maria
- volume
- 11482 LNCS
- pages
- 12 pages
- publisher
- Springer
- conference name
- 21st Scandinavian Conference on Image Analysis, SCIA 2019
- conference location
- Norrköping, Sweden
- conference dates
- 2019-06-11 - 2019-06-13
- external identifiers
-
- scopus:85066887999
- ISSN
- 0302-9743
- 1611-3349
- ISBN
- 9783030202040
- DOI
- 10.1007/978-3-030-20205-7_24
- language
- English
- LU publication?
- yes
- id
- a86e485e-5a07-4a78-be50-ef43a363ad54
- date added to LUP
- 2019-06-19 14:25:18
- date last changed
- 2024-06-25 19:32:29
@inproceedings{a86e485e-5a07-4a78-be50-ef43a363ad54, abstract = {{<p>In this paper we introduce a fast and robust structure-less alternative to full bundle adjustment. The method is based on optimizing algebraic errors for trilinear constraints from triplets of views. It is shown that the error generated by a triplet of views can be described by a fixed triangular matrix regardless of the number of feature correspondences between the views. The method has been evaluated on various real and synthetic datasets and shows good convergence properties with a large convergence basin and solutions that are close to the optimal solution. The method has been compared to Global Epipolar Adjustment, GEA, which is based on the bilinear constraint. It will be shown that the method can handle the degenerate configurations of GEA.</p>}}, author = {{Persson, Patrik and Åström, Kalle}}, booktitle = {{Image Analysis - 21st Scandinavian Conference, SCIA 2019, Proceedings}}, editor = {{Felsberg, Michael and Forssén, Per-Erik and Unger, Jonas and Sintorn, Ida-Maria}}, isbn = {{9783030202040}}, issn = {{0302-9743}}, keywords = {{Optimization; SFM; Structure-less bundle adjustment}}, language = {{eng}}, pages = {{287--298}}, publisher = {{Springer}}, series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}}, title = {{Global Trifocal Adjustment}}, url = {{http://dx.doi.org/10.1007/978-3-030-20205-7_24}}, doi = {{10.1007/978-3-030-20205-7_24}}, volume = {{11482 LNCS}}, year = {{2019}}, }