Globally-Optimal Contrast Maximisation for Event Cameras

TL;DR

This paper introduces a globally optimal contrast maximisation method for event cameras, enabling accurate motion estimation without initial guess dependency by using branch-and-bound optimisation.

Contribution

It presents the first globally optimal solutions for contrast maximisation in event camera motion estimation, removing the need for initial guesses and improving robustness.

Findings

Successfully applied to three motion estimation problems

Achieves globally optimal solutions with branch-and-bound

Outperforms existing methods reliant on initial guesses

Abstract

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.