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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Video Reconstruction | GoPro (test) | PSNR25.84 | 16 | |
| Single-image deblurring | Blur-DVS | PSNR19.02 | 11 | |
| Visual-Inertial Odometry | Event Camera Dataset | Translation Error (Boxes)0.7 | 6 | |
| Frame synthesis | Event-based sequences batch of N = 10,000 events | Frame Synthesis Time (ms)0.7 | 4 | |
| Video Reconstruction | Event Camera Dataset dynamic_6dof | Temporal Error3.32 | 4 | |
| Video Reconstruction | Event Camera Dataset boxes_6dof | Temporal Error3.37 | 4 | |
| Video Reconstruction | Event Camera Dataset poster_6dof | Temporal Error3.63 | 4 | |
| Video Reconstruction | Event Camera Dataset shapes_6dof | Temporal Error3.5 | 4 | |
| Video Reconstruction | Event Camera Dataset office_zigzag | Temporal Error3.18 | 4 | |
| Video Reconstruction | Event Camera Dataset slider_depth | Temporal Error2.14 | 4 |