Abstract
This paper presents an approach for the detection of critical camera configurations in unorganized image sets with (approximately) known internal camera parameters. Critical configurations are caused by an insufficient distance between cameras compared to the distance of the observed scene and can cause problems in triangulation-based structure from motion application.
We give a summary of existing techniques and propose a new approach for the detection of image pairs with a critical camera configuration based on classification using a random forest. To this end, several features characterizing the quality of the reconstructed 3D points as well as the estimated camera poses have been defined and evaluated for various configurations. The proposed approach is integrated into the structure from motion framework COLMAP demonstrating its potential on an independent real-world dataset.
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Notes
- 1.
The eigenvalues of a rotation matrix are \((1,e^{i\theta },e^{-i\theta })\), where the last two are complex conjugate with \(\theta \) as the angle of rotation. The eigenvector corresponding to the eigenvalue 1 is the axis of rotation.
- 2.
https://demuc.de/colmap, Version 3.5.
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Michelini, M., Mayer, H. (2020). Detection of Critical Camera Configurations for Structure from Motion Using Random Forest. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12046. Springer, Cham. https://doi.org/10.1007/978-3-030-41404-7_36
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