Research map / 3D vision / Depth and stereo
A Unifying Contrast Maximization Framework for Event Cameras, With Applications to Motion, Depth, and Optical Flow Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| maximize contrast by warping events along candidate point trajectories | Event-camera motion estimation · boxes rotation sequence, Event Camera Dataset | angular velocity error: approximately 20 °/s RMS error; 670 ◦/s peak excursions | Helped |
| use contrast-maximizing motion-corrected event images instead of uncorrected event images | Visual-inertial algorithm · brick-patterned ground sequence | 3D point-map quality | Helped |
| align events using the estimated rotational motion rather than no motion correction | Rotational motion estimation · Event Camera Dataset | image sharpness | Helped |
| select depth by maximizing the contrast of warped events | Depth estimation · slider depth sequence, Event Camera Dataset | event alignment | Helped |
Closest papers by meaning
- EF-3DGS: Event-Aided Free-Trajectory 3D Gaussian Splatting (NeurIPS 2025 · similarity 0.86)
- Injecting Frame-Event Complementary Fusion into Diffusion for Optical Flow in Challenging Scenes (NeurIPS 2025 · similarity 0.86)
- Event-Driven Dynamic Scene Depth Completion (NeurIPS 2025 · similarity 0.86)
- An N-Point Linear Solver for Line and Motion Estimation with Event Cameras (CVPR 2024 · similarity 0.86)
- EDCFlow: Exploring Temporally Dense Difference Maps for Event-based Optical Flow Estimation (CVPR 2025 · similarity 0.85)
- TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion (CVPR 2025 · similarity 0.85)
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