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Continuous-time Intensity Estimation Using Event Cameras

About

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.

Cedric Scheerlinck, Nick Barnes, Robert Mahony• 2018

Related benchmarks

TaskDatasetResultRank
Video ReconstructionGoPro (test)
PSNR25.84
16
Single-image deblurringBlur-DVS
PSNR19.02
11
Visual-Inertial OdometryEvent Camera Dataset
Translation Error (Boxes)0.7
6
Frame synthesisEvent-based sequences batch of N = 10,000 events
Frame Synthesis Time (ms)0.7
4
Video ReconstructionEvent Camera Dataset dynamic_6dof
Temporal Error3.32
4
Video ReconstructionEvent Camera Dataset boxes_6dof
Temporal Error3.37
4
Video ReconstructionEvent Camera Dataset poster_6dof
Temporal Error3.63
4
Video ReconstructionEvent Camera Dataset shapes_6dof
Temporal Error3.5
4
Video ReconstructionEvent Camera Dataset office_zigzag
Temporal Error3.18
4
Video ReconstructionEvent Camera Dataset slider_depth
Temporal Error2.14
4
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