A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation

By Guillermo Gallego, Henri Rebecq, Davide Scaramuzza. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018

434 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/f1d332de6c042836f036906f60d28f186df59c02

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Citations in the corpus

End-to-End Learning of Representations for Asynchronous Event-Based Data (2019, background): “Other works have tackled this task by finding the optimal event alignments when projected onto a frame [20, 65].”
Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars (2018, background): “Indeed, event cameras present many advantages in all tasks related to motion estimation [2, 3, 4, 5, 6].”
Event-Based Moving Object Detection and Tracking (2018, background): “[9] proposes a global 3D motion estimation approach for 3D rotation, and in [8] extensions of the approach are discussed.”
Event-Based Vision: A Survey (2019, methodology): “Motion-compensated event image [122], [123]: is a representation that depends not only on events but also on motion hypothesis.”