Timo Stoffregen — accepted AI-conference papers

2020

Reducing the Sim-to-Real Gap for Event Cameras

Timo Stoffregen, Cedric Scheerlinck, Davide Scaramuzza, Tom Drummond, Nick Barnes, Lindsay Kleeman +1

ECCV 2020poster

Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called `events' with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional cameras would fail. Recent work has demonstrated impressive resul…

Cited by 206 Source ↗PDF ↗code ↗Scholar ↗

2019

Event Cameras, Contrast Maximization and Reward Functions: An Analysis

Timo Stoffregen, Lindsay Kleeman

CVPR 2019poster

Event cameras asynchronously report timestamped changes in pixel intensity and offer advantages over conventional raster scan cameras in terms of low-latency, low redundancy sensing and high dynamic range. In recent years, much of research in event based vision has been focused on performing tasks s…

Cited by 91 PDF ↗Scholar ↗

2019

Event-Based Motion Segmentation by Motion Compensation

Timo Stoffregen, Guillermo Gallego, Tom Drummond, Lindsay Kleeman, Davide Scaramuzza

ICCV 2019poster

In contrast to traditional cameras, whose pixels have a common exposure time, event-based cameras are novel bio-inspired sensors whose pixels work independently and asynchronously output intensity changes (called "events"), with microsecond resolution. Since events are caused by the apparent motion…

Cited by 188 PDF ↗Scholar ↗