Event cameras provide asynchronous, data-driven measurements of local temporal contrast over a large dynamic range with extremely high temporal resolution. Conventional cameras capture low-frequency reference intensity information. These two sensor modalities provide complementary information. We propose a computationally efficient, asynchronous filter that continuously fuses image frames and events into a single high-temporal-resolution, high-dynamic-range image state. In absence of conventional image frames, the filter can be run on events only. We present experimental results on high-speed, high-dynamic-range sequences, as well as on new ground truth datasets we generate to demonstrate the proposed algorithm outperforms existing state-of-the-art methods. Code, Datasets and Video: https://cedric-scheerlinck.github.io/continuous-time-intensity-estimation.
CITATION STYLE
Scheerlinck, C., Barnes, N., & Mahony, R. (2019). Continuous-Time Intensity Estimation Using Event Cameras. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11365 LNCS, pp. 308–324). Springer Verlag. https://doi.org/10.1007/978-3-030-20873-8_20
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