Physics

Event (particle physics)

Mathematics

Motion estimation

Motion (physics)

Image (mathematics)

Structure from motion

Image plane

Motion field

Artificial intelligence

Optical flow

Set (abstract data type)

Programming language

Geometry

Contrast (vision)

Computer science

Quantum mechanics

Point (geometry)

Computer vision

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.

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