Publications

Zhaoning Sun*

Equal contribution

, Nico Messikommer*

Equal contribution

, Daniel Gehrig, Davide Scaramuzza (2022). ESS: Learning Event-based Semantic Segmentation from Still Images. European Conf. on Computer Vision (ECCV).

Code Dataset PDF Video

Simon Schaefer*

Equal contribution

, Daniel Gehrig*

Equal contribution

, Davide Scaramuzza (2022). AEGNN: Asynchronous Event-based Graph Neural Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Code PDF Project Video

Daniel Gehrig

Equal contribution

, Davide Scaramuzza

Equal contribution

(2022). Are High-Resolution Event Cameras Really Needed?. arXiv.

PDF Project Video

Nico Messikommer

Equal contribution

, Daniel Gehrig

Equal contribution

, Mathias Gehrig, Davide Scaramuzza (2022). Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation. IEEE Robot. Autom. Lett. (RA-L).

Code PDF Video

Mathias Gehrig*

Equal contribution

, Mario Millhäusler

Equal contribution

, Daniel Gehrig, Davide Scaramuzza (2021). E-RAFT: Dense Optical Flow from Event Cameras. 3D Vision (3DV).

Code Dataset PDF Project Video

Stepan Tulyakov*

Equal contribution

, Daniel Gehrig*

Equal contribution

, Stamatios Georgoulis, Julius Erbach, Mathias Gehrig, Yuanyou Li, Davide Scaramuzza (2021). Time Lens: Event-based Video Frame Interpolation . IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

Code Dataset PDF Project Slides

Manasi Muglikar*

Equal contribution

, Mathias Gehrig*

Equal contribution

, Daniel Gehrig, Davide Scaramuzza (2021). How to Calibrate Your Event Camera. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Code PDF Video

Mathias Gehrig

Equal contribution

, Willem Aarents

Equal contribution

, Daniel Gehrig, Davide Scaramuzza (2021). DSEC: A Stereo Event Camera Dataset for Driving Scenarios. IEEE Robot. Autom. Lett. (RA-L).

Code Dataset PDF Project Slides Video

Daniel Gehrig

Equal contribution

, Michelle Rüegg

Equal contribution

, Mathias Gehrig, Javier Hidalgo-Carrio, Davide Scaramuzza (2021). Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction. IEEE Robot. Autom. Lett. (RA-L).