Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular Image Sequences
Principal investigators: Florian Becker, Frank Lenzen, Jörg H. Kappes, Christoph SchnörrOverview
We present an approach to jointly estimating camera motion and dense structure of a static scene in terms of depth maps from monocular image sequences in driver-assistance scenarios. At each instant of time, only two consecutive frames are processed as input data of a joint estimator that fully exploits second-order information of the corresponding optimization problem and effectively copes with the non-convexity due to both the imaging geometry and the manifold of motion parameters. Additionally, carefully designed Gaussian approximations enable probabilistic inference based on locally varying confidence and globally varying sensitivity due to the epipolar geometry, with respect to the high-dimensional depth map estimation. Embedding the resulting joint estimator in an online recursive framework achieves a pronounced spatio-temporal filtering effect and robustness.
Becker et al. (2013) (an extension of Becker et al (2011)) - and the corresponding supplemental material.
Acknowledgments
The research presented here was conducted at the Heidelberg Collaboratory for Image Processing (HCI). HCI is supported by the DFG, Heidelberg University and industrial partners. The authors thank Dr. W. Niehsen, Robert Bosch GmbH.Publications
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Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences
Florian Becker, Frank Lenzen, Jörg H. Kappes and Christoph Schnörr
In International Journal of Computer Vision, 105:269-297, 2013. Springer.
[PDF (preprint)] [PDF] [supplemental material] [BIB (bibtex)] -
Variational Recursive Joint Estimation of Dense Scene Structure and Camera Motion from Monocular High Speed Traffic Sequences
Florian Becker, Frank Lenzen, Jörg H. Kappes and Christoph Schnörr
In Proceedings of the 2011 International Conference on Computer Vision, pages 1692-1699, 2011. IEEE Computer Society.
[PDF (preprint)] [PDF] [supplemental material] [BIB (bibtex)]
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