This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Feature Field (F3). We learn this representation by predicting future events from past events and show that it preserves scene structure and motion information. F3 exploits the sparsity of event data and is robust to noise and variations in event rates. It can be computed efficiently using ideas from multi-resolution hash encoding and deep sets - achieving 120 Hz at HD and 440 Hz at VGA resolutions. F3 represents events within a contiguous spatiotemporal volume as a multi-channel image, enabling a range of downstream tasks. We obtain state-of-the-art performance on optical flow estimation, semantic segmentation, and monocular metric depth estimation, on data from three robotic platforms (a car, a quadruped robot and a flying platform), across different lighting conditions (daytime, nighttime), environments (indoors, outdoors, urban, as well as off-road) and dynamic vision sensors (resolutions and event rates). Our implementations can predict these tasks at 25-75 Hz at HD resolution.
@article{arxiv.2509.25146,
title = {Fast Feature Field ($\text{F}^3$): A Predictive Representation of Events},
author = {Richeek Das and Kostas Daniilidis and Pratik Chaudhari},
journal= {arXiv preprint arXiv:2509.25146},
year = {2025}
}