164 citations · 323 across the 19 of their papers we have counts for
6 papers · 1 filter
Bridging the Gap between Events and Frames through Unsupervised Domain Adaptation
Nico Messikommer, Daniel Gehrig, Mathias Gehrig +1
Reliable perception during fast motion maneuvers or in high dynamic range environments is crucial for robotic systems. Since event cameras are robust to these challenging condition…
E-RAFT: Dense Optical Flow from Event Cameras
Mathias Gehrig, Mario Millhäusler, Daniel Gehrig +1
We propose to incorporate feature correlation and sequential processing into dense optical flow estimation from event cameras. Modern frame-based optical flow methods heavily rely…
TimeLens: Event-based Video Frame Interpolation
Stepan Tulyakov, Daniel Gehrig, Stamatios Georgoulis +4
State-of-the-art frame interpolation methods generate intermediate frames by inferring object motions in the image from consecutive key-frames. In the absence of additional informa…
How to Calibrate Your Event Camera
Manasi Muglikar, Mathias Gehrig, Daniel Gehrig +1
We propose a generic event camera calibration framework using image reconstruction. Instead of relying on blinking LED patterns or external screens, we show that neural-network-bas…
DSEC: A Stereo Event Camera Dataset for Driving Scenarios
Mathias Gehrig, Willem Aarents, Daniel Gehrig +1
Once an academic venture, autonomous driving has received unparalleled corporate funding in the last decade. Still, the operating conditions of current autonomous cars are mostly r…
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction
Daniel Gehrig, Michelle Rüegg, Mathias Gehrig +2
Event cameras are novel vision sensors that report per-pixel brightness changes as a stream of asynchronous "events". They offer significant advantages compared to standard cameras…