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Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares (bibtex)
by D Muhle, L Koestler, KM Jatavallabhula and D Cremers
Reference:
Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares (D Muhle, L Koestler, KM Jatavallabhula and D Cremers), In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023. ([project page])
Bibtex Entry:
@inproceedings{muhle2023learning,
title = {Learning Correspondence Uncertainty via Differentiable Nonlinear Least Squares},
author = {D Muhle and L Koestler and KM Jatavallabhula and D Cremers},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
eprint = {2305.09527},
eprinttype = {arXiv},
eprintclass = {cs.CV},
pages = {13102--13112},
year = {2023},
keywords = {pnec, vo, vslam, deep learning},
}
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