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@inproceedings{koestler2020learning,
 author = {L. Koestler and N. Yang and R. Wang and D. Cremers},
 title = {Learning Monocular 3D Vehicle Detection without 3D Bounding Box Labels},
 booktitle = {Proceedings of the German Conference on Pattern Recognition (GCPR)},
 year = {2020},
}

@inproceedings{koestler2021tandem,
 author = {L Koestler and N Yang and N Zeller and D Cremers},
 title = {TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo},
 booktitle = {Conference on Robot Learning (CoRL)},
 year = {2021},
 eprint = {2111.07418},
 eprinttype = {arXiv},
 award = {3DV'21 Best Demo Award},
 keywords = {tandem, odometry, VO, SLAM, vslam, dense reconstruction, mvs},
}

@inproceedings{muhle2022pnec,
 author = {D Muhle and L Koestler and N Demmel and F Bernard and D Cremers},
 title = {The Probabilistic Normal Epipolar Constraint for Frame-To-Frame Rotation Optimization under Uncertain Feature Positions},
 eprint = {2204.02256},
 eprinttype = {arXiv},
 eprintclass = {cs.CV},
 booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
 year = {2022},
 keywords = {pnec, vo, vslam},
}

@inproceedings{koestler2022intrinsic,
 author = {L Koestler and D Grittner and M Moeller and D Cremers and Z Lähner},
 title = {Intrinsic Neural Fields: Learning Functions on Manifolds},
 booktitle = {European Conference on Computer Vision (ECCV)},
 year = {2022},
 eprint = {2203.07967},
 eprinttype = {arXiv},
 keywords = {Neural Fields, Shape Analysis, Geometry Processing},
}

@inproceedings{hofherr2023neuralPhysParam,
 author = {F Hofherr and L Koestler and F Bernard and D Cremers},
 title = {Neural Implicit Representations for Physical Parameter Inference from a Single Video},
 booktitle = {IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
 year = {2023},
 eprint = {2204.14030},
 eprinttype = {arXiv},
 keywords = {Neural Fields, Physical Parameter Estimation, Geometry Processing},
 award = {Spotlight Presentation},
}

@article{klenk2023nerf,
 title = {E-nerf: Neural radiance fields from a moving event camera},
 author = {S Klenk and L Koestler and D Scaramuzza and D Cremers},
 journal = {IEEE Robotics and Automation Letters},
 volume = {8},
 number = {3},
 pages = {1587--1594},
 year = {2023},
 publisher = {IEEE},
 keywords = {event camera, enerf, nerf},
}

@inproceedings{klenk2022masked,
 title = {Masked Event Modeling: Self-Supervised Pretraining for Event Cameras},
 author = {S Klenk and D Bonello and L Koestler and N Araslanov and D Cremers},
 journal = {arXiv preprint arXiv:2212.10368},
 year = {2024},
 eprint = {2212.10368},
 eprinttype = {arXiv},
 eprintclass = {cs.CV},
 booktitle = {{IEEE Winter Conference on Applications of Computer Vision (WACV)}},
 month = {January},
 address = {Hawaii, USA},
}

@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},
}

@inproceedings{klenk2024deep,
 title = {Deep event visual odometry},
 author = {S Klenk and M Motzet and L Koestler and D Cremers},
 booktitle = {2024 International conference on 3D vision (3DV)},
 eprint = {2312.09800},
 eprinttype = {arXiv},
 eprintclass = {cs.CV},
 pages = {739--749},
 year = {2024},
 organization = {IEEE},
}