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Daniel Gehrig
Daniel Gehrig
Ph.D. candidate, University of Zurich
在 ifi.uzh.ch 的电子邮件经过验证
标题
引用次数
引用次数
年份
ESIM: an open event camera simulator
H Rebecq, D Gehrig, D Scaramuzza
Conference on robot learning, 969-982, 2018
1932018
End-to-end learning of representations for asynchronous event-based data
D Gehrig, A Loquercio, KG Derpanis, D Scaramuzza
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019
1452019
Asynchronous, Photometric Feature Tracking using Events and Frames
D Gehrig
Robotics and Perception Group, University of Zurich, 2018
892018
Asynchronous, photometric feature tracking using events and frames
D Gehrig, H Rebecq, G Gallego, D Scaramuzza
Proceedings of the European Conference on Computer Vision (ECCV), 750-765, 2018
892018
Video to events: Recycling video datasets for event cameras
D Gehrig, M Gehrig, J Hidalgo-Carrió, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
802020
Fast image reconstruction with an event camera
C Scheerlinck, H Rebecq, D Gehrig, N Barnes, R Mahony, D Scaramuzza
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020
772020
EKLT: Asynchronous photometric feature tracking using events and frames
D Gehrig, H Rebecq, G Gallego, D Scaramuzza
International Journal of Computer Vision 128 (3), 601-618, 2020
752020
Event-based Asynchronous Sparse Convolutional Networks
N Messikommer*, D Gehrig*, A Loquercio, D Scaramuzza
Proceedings of the European Conference on Computer Vision (ECCV), 2020
492020
Dsec: A stereo event camera dataset for driving scenarios
M Gehrig, W Aarents, D Gehrig, D Scaramuzza
IEEE Robotics and Automation Letters 6 (3), 4947-4954, 2021
452021
Learning Monocular Dense Depth from Events
J Hidalgo-Carrió, D Gehrig, D Scaramuzza
International Conference on 3D Vision (3DV), 2020
352020
Combining events and frames using recurrent asynchronous multimodal networks for monocular depth prediction
D Gehrig, M Rüegg, M Gehrig, J Hidalgo-Carrió, D Scaramuzza
IEEE Robotics and Automation Letters 6 (2), 2822-2829, 2021
312021
TimeLens: Event-based Video Frame Interpolation
S Tulyakov*, D Gehrig*, S Georgoulis, J Erbach, M Gehrig, Y Li, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
302021
E-raft: Dense optical flow from event cameras
M Gehrig, M Millhäusler, D Gehrig, D Scaramuzza
2021 International Conference on 3D Vision (3DV), 197-206, 2021
14*2021
How to calibrate your event camera
M Muglikar, M Gehrig, D Gehrig, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
72021
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
N Messikommer, D Gehrig, M Gehrig, D Scaramuzza
IEEE Robotics and Automation Letters 7 (2), 3515-3522, 2022
42022
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
S Tulyakov, A Bochicchio, D Gehrig, S Georgoulis, Y Li, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
42022
Exploring Event Camera-based Odometry for Planetary Robots
F Mahlknecht, D Gehrig, J Nash, FM Rockenbauer, B Morrell, J Delaune, ...
IEEE Robotics and Automation Letters, 2022
22022
Multi-Bracket High Dynamic Range Imaging with Event Cameras
N Messikommer, S Georgoulis, D Gehrig, S Tulyakov, J Erbach, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
22022
Are High-Resolution Event Cameras Really Needed?
D Gehrig, D Scaramuzza
arXiv preprint arXiv:2203.14672, 2022
12022
AEGNN: Asynchronous Event-based Graph Neural Networks
S Schaefer, D Gehrig, D Scaramuzza
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022
12022
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