A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation
This work addresses the challenge of diverse vision tasks for event camera applications, presenting a novel framework that is not incremental but offers a new approach.
The authors tackled the problem of solving multiple computer vision tasks with event cameras, such as motion, depth, and optical flow estimation, by introducing a unifying framework based on contrast maximization of warped events, which accurately recovers motion parameters and produces high dynamic range images.
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 parameters of the problem, our framework produces motion-corrected edge-like images with high dynamic range that can be used for further scene analysis. The proposed method is not only simple, but more importantly, it is, to the best of our knowledge, the first method that can be successfully applied to such a diverse set of important vision tasks with event cameras.