Continuous-time Intensity Estimation Using Event Cameras
This work addresses the challenge of high-speed, high-dynamic-range imaging for applications like robotics and computer vision, though it appears incremental as it builds on existing sensor fusion techniques.
The paper tackles the problem of estimating continuous-time intensity from event cameras and conventional images, proposing a filter that fuses both modalities to achieve high-temporal-resolution and high-dynamic-range imaging, with experimental results showing it outperforms existing state-of-the-art methods on new datasets.
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.