Globally-Optimal Contrast Maximisation for Event Cameras
This work addresses the need for new algorithms to process event camera data, offering globally optimal solutions for motion estimation that improve reliability in applications like robotics or autonomous systems, though it is incremental in optimizing existing contrast maximization methods.
The paper tackles motion estimation problems for event cameras by modeling event flow with a general homographic warping and formulating the objective as contrast maximization, deriving globally optimal solutions that eliminate dependency on good initial guesses and demonstrating practical validity across three motion estimation problems.
Event cameras are bio-inspired sensors that perform well in challenging illumination conditions and have high temporal resolution. However, their concept is fundamentally different from traditional frame-based cameras. The pixels of an event camera operate independently and asynchronously. They measure changes of the logarithmic brightness and return them in the highly discretised form of time-stamped events indicating a relative change of a certain quantity since the last event. New models and algorithms are needed to process this kind of measurements. The present work looks at several motion estimation problems with event cameras. The flow of the events is modelled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of warped events. Our core contribution consists of deriving globally optimal solutions to these generally non-convex problems, which removes the dependency on a good initial guess plaguing existing methods. Our methods rely on branch-and-bound optimisation and employ novel and efficient, recursive upper and lower bounds derived for six different contrast estimation functions. The practical validity of our approach is demonstrated by a successful application to three different event camera motion estimation problems.