351 citations · 1.2k across the 28 of their papers we have counts for
32 papers · 1 filter
Pushing the Limits of Asynchronous Graph-based Object Detection with Event Cameras
Daniel Gehrig, Davide Scaramuzza
State-of-the-art machine-learning methods for event cameras treat events as dense representations and process them with conventional deep neural networks. Thus, they fail to mainta…
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion
Stepan Tulyakov, Alfredo Bochicchio, Daniel Gehrig +3
Recently, video frame interpolation using a combination of frame- and event-based cameras has surpassed traditional image-based methods both in terms of performance and memory effi…
Are High-Resolution Event Cameras Really Needed?
Daniel Gehrig, Davide Scaramuzza
Due to their outstanding properties in challenging conditions, event cameras have become indispensable in a wide range of applications, ranging from automotive, computational photo…
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…