Latest revision as of 21:08, 1 February 2021

Abstract

The quality and speed of Structure from Motion (SfM) methods depend significantly on the camera model chosen for the reconstruction. In most of the SfM pipelines, the camera model is manually chosen by the user. In this paper, we present a new automatic method for camera model selection in large scale SfM that is based on efficient uncertainty evaluation. We first perform an extensive comparison of classical model selection based on known Information Criteria and show that they do not provide sufficiently accurate results when applied to camera model selection. Then we propose a new Accuracy-based Criterion, which evaluates an efficient approximation of the uncertainty of the estimated parameters in tested models. Using the new criterion, we design a camera model selection method and fine-tune it by machine learning. Our simulated and real experiments demonstrate a significant increase in reconstruction quality as well as a considerable speedup of the SfM process.

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The different versions of the original document can be found in:

• http://dx.doi.org/10.5281/zenodo.4024106 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.5281/zenodo.4024107 under the license https://creativecommons.org/licenses/by/4.0/legalcode

• https://zenodo.org/record/4024107 under the license https://creativecommons.org/licenses/by/4.0/legalcode

• https://zenodo.org/record/4024107/files/Uncertainty_Based_Camera_Model_Selection.pdf

• http://xplorestaging.ieee.org/ielx7/9142308/9156271/09156313.pdf?arnumber=9156313,

http://dx.doi.org/10.1109/cvpr42600.2020.00603 under the license cc-by

• https://dblp.uni-trier.de/db/conf/cvpr/cvpr2020.html#PolicSAKP20,

http://openaccess.thecvf.com/content_CVPR_2020/papers/Polic_Uncertainty_Based_Camera_Model_Selection_CVPR_2020_paper.pdf,

https://ieeexplore.ieee.org/document/9156313,

http://openaccess.thecvf.com/content_CVPR_2020/html/Polic_Uncertainty_Based_Camera_Model_Selection_CVPR_2020_paper.html,

https://academic.microsoft.com/#/detail/3034634220

DOIS: 10.5281/zenodo.4024106 10.1109/cvpr42600.2020.00603 10.5281/zenodo.4024107