MLESAC: a new robust estimator with application to estimating image geometry
Computer Vision and Image Understanding - Special issue on robusst statistical techniques in image understanding
A Flexible New Technique for Camera Calibration
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complete Calibration of a Multi-camera Network
OMNIVIS '00 Proceedings of the IEEE Workshop on Omnidirectional Vision
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A convenient multicamera self-calibration for virtual environments
Presence: Teleoperators and Virtual Environments
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Modeling the World from Internet Photo Collections
International Journal of Computer Vision
A General Algorithm to Recover External Camera Parameters from Pairwise Camera Calibrations
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
SBA: A software package for generic sparse bundle adjustment
ACM Transactions on Mathematical Software (TOMS)
Intrinsic and extrinsic active self-calibration of multi-camera systems
Machine Vision and Applications
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We propose a novel minimum uncertainty approach to relative pose selection for multi-camera self-calibration. We show how this discrete global optimization problem can be expressed as a shortest triangle paths problem. For the latter, we present an efficient algorithm and prove its correctness. It has several advantages compared to a similar approach of Vergés-Llahí, Moldovan and Wada. In quantitative experiments on publically available data, we show that our relative pose selection method provides improvements compared to naive, random and greedy selection, without and with subsequent bundle adjustment.