ESIM: an Open Event Camera Simulator

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Henri Rebecq, Daniel Gehrig, Davide Scaramuzza ;
Proceedings of The 2nd Conference on Robot Learning, PMLR 87:969-982, 2018.

Abstract

Event cameras are revolutionary sensors that work radically differently from standard cameras. Instead of capturing intensity images at a fixed rate, event cameras measure changes of intensity asynchronously, in the form of a stream of events, which encode per-pixel brightness changes. In the last few years, their outstanding properties (asynchronous sensing, no motion blur, high dynamic range) have led to exciting vision applications, with very low-latency and high robustness. However, these sensors are still scarce and expensive to get, slowing down progress of the research community. To address these issues, there is a huge demand for cheap, high-quality synthetic, labeled event for algorithm prototyping, deep learning and algorithm benchmarking. The development of such a simulator, however, is not trivial since event cameras work fundamentally differently from frame-based cameras. We present the first event camera simulator that can generate a large amount of reliable event data. The key component of our simulator is a theoretically sound, adaptive rendering scheme that only samples frames when necessary, through a tight coupling between the rendering engine and the event simulator. We release an open source implementation of our simulator.

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