Publication: A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
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Gallego, G., Rebecq, H., & Scaramuzza, D. (2018). A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation. 3867–3876. https://doi.org/10.1109/cvpr.2018.00407
Abstract
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We present a unifying framework to solve several computer vision problems with event cameras: motion, depth and optical flow estimation. The main idea of our framework is to find the point trajectories on the image plane that are best aligned with the event data by maximizing an objective function: the contrast of an image of warped events. Our method implicitly handles data association between the events, and therefore, does not rely on additional appearance information about the scene. In addition to accurately recovering the motion
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- Gallego, Guillermo
- Rebecq, Henri
- Scaramuzza, Davide
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Citations
Gallego, G., Rebecq, H., & Scaramuzza, D. (2018). A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation. 3867–3876. https://doi.org/10.1109/cvpr.2018.00407
