Revisiting Rotation Averaging : Uncertainties and Robust Losses
(2023) 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023 In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2023-June. p.17215-17224- Abstract
In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results... (More)
In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results superior to baselines, in terms of accuracy, on large-scale public benchmarks. The code is public. https://github.com/zhangganlin/GlobalSfMpy
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- author
- Zhang, Ganlin ; Larsson, Viktor LU and Barath, Daniel
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- 3D from multi-view and sensors
- host publication
- Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
- series title
- Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- volume
- 2023-June
- pages
- 10 pages
- publisher
- IEEE Computer Society
- conference name
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023
- conference location
- Vancouver, Canada
- conference dates
- 2023-06-18 - 2023-06-22
- external identifiers
-
- scopus:85173945874
- ISSN
- 1063-6919
- ISBN
- 9798350301298
- DOI
- 10.1109/CVPR52729.2023.01651
- language
- English
- LU publication?
- yes
- id
- cb64a3a3-b4d2-4caa-b1c6-3472dce56bbf
- date added to LUP
- 2023-12-15 09:57:17
- date last changed
- 2023-12-15 09:59:12
@inproceedings{cb64a3a3-b4d2-4caa-b1c6-3472dce56bbf, abstract = {{<p>In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. Such uncertainties are obtained for free by considering the Jacobians of two-view refinements. Moreover, we explore integrating a variant of the MAGSAC loss into the rotation averaging problem, instead of using classical robust losses employed in current frameworks. The proposed method leads to results superior to baselines, in terms of accuracy, on large-scale public benchmarks. The code is public. https://github.com/zhangganlin/GlobalSfMpy</p>}}, author = {{Zhang, Ganlin and Larsson, Viktor and Barath, Daniel}}, booktitle = {{Proceedings - 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2023}}, isbn = {{9798350301298}}, issn = {{1063-6919}}, keywords = {{3D from multi-view and sensors}}, language = {{eng}}, pages = {{17215--17224}}, publisher = {{IEEE Computer Society}}, series = {{Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition}}, title = {{Revisiting Rotation Averaging : Uncertainties and Robust Losses}}, url = {{http://dx.doi.org/10.1109/CVPR52729.2023.01651}}, doi = {{10.1109/CVPR52729.2023.01651}}, volume = {{2023-June}}, year = {{2023}}, }