Direkt zum Inhalt springen
Computer Vision Group
TUM Department of Informatics
Technical University of Munich

Technical University of Munich



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

Accepted at CVPR 2022. Under construction.

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

**For up-to-date information, code, and data please visit our [GitHub repository]


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.




Export as PDF, XML, TEX or BIB

Conference and Workshop Papers
[]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]
Powered by bibtexbrowser
Export as PDF, XML, TEX or BIB

Rechte Seite

Informatik IX
Chair of Computer Vision & Artificial Intelligence

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

Follow us on:
CVG Group DVL Group



In April 2022 Jürgen Sturm, Christian Kerl and Daniel Cremers were featured among the top 10 most influential scholars in robotics of the last decade.


We have open PhD and postdoc positions! To apply, please use our application form.


We have six papers accepted to CVPR 2022 in New Orleans!


We have two papers accepted to ICRA 2022 - congrats to Lukas von Stumberg, Qing Cheng and Niclas Zeller!