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TUM School of Computation, Information and Technology
Technical University of Munich

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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

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09.06.2024
GCPR / VMV 2024

GCPR / VMV 2024

We are organizing GCPR / VMV 2024 this fall.

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

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The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions

CVPR 2022

Authors: Dominik Muhle*, Lukas Koestler*, Publications, Florian Bernard, Daniel Cremers

For up-to-date information, code, and data please visit our Github repository

Abstract

The estimation of the relative pose of two camera views is a fundamental problem in computer vision. Kneip et al. proposed to solve this problem by introducing the normal epipolar constraint (NEC). However, their approach does not take into account uncertainties, so that the accuracy of the estimated relative pose is highly dependent on accurate feature positions in the target frame. In this work, we introduce the probabilistic normal epipolar constraint (PNEC) that overcomes this limitation by accounting for anisotropic and inhomogeneous uncertainties in the feature positions. To this end, we propose a novel objective function, along with an efficient optimization scheme that effectively minimizes our objective while maintaining real-time performance. In experiments on synthetic data, we demonstrate that the novel PNEC yields more accurate rotation estimates than the original NEC and several popular relative rotation estimation algorithms. Furthermore, we integrate the proposed method into a state-of-the-art monocular rotation-only odometry system and achieve consistently improved results for the real-world KITTI dataset.

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Conference and Workshop Papers
2023
[]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] [arXiv:2305.09527] [pdf]
2022
[]The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions (D Muhle, L Koestler, N Demmel, F Bernard and D Cremers), In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2022. ([project page]) [bibtex] [arXiv:2204.02256] [pdf]
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Informatik IX
Computer Vision Group

Boltzmannstrasse 3
85748 Garching info@vision.in.tum.de

Follow us on:

News

09.06.2024
GCPR / VMV 2024

GCPR / VMV 2024

We are organizing GCPR / VMV 2024 this fall.

04.03.2024

We have twelve papers accepted to CVPR 2024. Check our publication page for more details.

18.07.2023

We have four papers accepted to ICCV 2023. Check out our publication page for more details.

02.03.2023

CVPR 2023

We have six papers accepted to CVPR 2023. Check out our publication page for more details.

15.10.2022

NeurIPS 2022

We have two papers accepted to NeurIPS 2022. Check out our publication page for more details.

More