Secrets of Edge-Informed Contrast Maximization for Event-Based Vision

Pritam P. Karmokar, Quan H. Nguyen, William J. Beksi; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 630-639

Event cameras capture the motion of intensity gradients (edges) in the image plane in the form of rapid asynchronous events. When accumulated in 2D histograms these events depict overlays of the edges in motion consequently obscuring the spatial structure of the generating edges. Contrast maximization (CM) is an optimization framework that can reverse this effect and produce sharp spatial structures that resemble the moving intensity gradients by estimating the motion trajectories of the events. Nonetheless CM is still an underexplored area of research with avenues for improvement. In this paper we propose a novel hybrid approach that extends CM from uni-modal (events only) to bi-modal (events and edges). We leverage the underpinning concept that given a reference time optimally warped events produce sharp gradients consistent with the moving edge at that time. Specifically we formalize a correlation-based objective to aid CM and provide key insights into the incorporation of multiscale and multireference techniques. Moreover our edge-informed CM method yields superior sharpness scores and establishes new state-of-the-art event optical flow benchmarks on the MVSEC DSEC and ECD datasets.