Title:

Efficient Data-driven Perception with Event Cameras

Abstract:

Event cameras are bio-inspired sensors that perceive the environment in an entirely different way. Instead of measuring synchronous frames of absolute intensity at fixed intervals, they only measure changes in intensity and do this independently for each pixel, resulting in an asynchronous stream of events. Events thus carry only the compressed visual signal but do this with a micro-second-level latency and temporal resolution, negligible motion blur, and high dynamic range while consuming low power and using low bandwidth. However, due to their working principle, event cameras output sparse and asynchronous data, which are not directly compatible with standard computer vision algorithms designed for dense frames. Therefore the development of new algorithms to process events, and leverage the advantages of these cameras is at the forefront of active research in event-based vision. In this talk, we will discuss ways to leverage the advantages of event cameras for high-speed robotics, and low-data computational photography. Finally, we will touch upon ways to enhance the efficiency of deep learning-based algorithms with novel asynchronous neural networks that take advantage of the spatiotemporal sparsity in event data.

Bio:

Daniel Gehrig is currently a postdoctoral researcher under the SNF Fellowship at the GRASP Lab at the University of Pennsylvania (UPenn) supervised by Prof. Kostas Daniilidis and Prof. Pratik Chaudhari. He obtained his Ph.D. in 2023 from the University of Zurich (UZH) in Switzerland, while working on computer vision and robotics research with event cameras at the Robotics and Perception Group (RPG). For his work, his Ph.D. was awarded with highest distinction, and the UZH annual award. Before that he completed his master’s studies in 2018 in in Mechanical Engineering at ETH Zurich, where he achieved with the highest possible score, and was thus awarded the Willi Studer Prize. During his studies, he came into contact with event-based vision while doing his master’s thesis on event- and frame-based feature tracking at the Robotics and Perception Group for which he was awarded the ETH Medal for the best master’s thesis of the year. His work has been featured prominently in Nature, IEEE Spectrum and on popular channels like Two Minute Papers.