# Towards Anytime Optical Flow Estimation with Event Cameras

## TL;DR

This paper introduces a new method for high-speed optical flow estimation using event cameras, enabling real-time motion tracking with minimal latency.

## Contribution

The paper introduces EVA-Flow, a novel network for event-based optical flow estimation with a low-latency event representation and an unsupervised loss function.

## Key findings

- EVA-Flow achieves super-low-latency (5 ms) and time-dense motion estimation (200 Hz).
- The proposed method demonstrates strong generalization across multiple datasets.
- The rectified flow warp loss enables unsupervised assessment of intermediate optical flow.

## Abstract

Event cameras respond to changes in log-brightness at the millisecond level, making them ideal for optical flow estimation. However, existing datasets from event cameras provide only low-frame-rate ground truth for optical flow, limiting the research potential of event-driven optical flow. To address this challenge, we introduce a low-latency event representation, unified voxel grid (UVG), and propose EVA-Flow, an EVent-based Anytime Flow estimation network to produce high-frame-rate event optical flow with only low-frame-rate optical flow ground truth for supervision. Furthermore, we propose rectified flow warp loss (RFWL) for the unsupervised assessment of intermediate optical flow. A comprehensive variety of experiments on MVSEC, DESC, and our EVA-FlowSet demonstrates that EVA-Flow achieves competitive performance, super-low-latency (5 ms), time-dense motion estimation (200 Hz), and strong generalization.