EvtSlowTV -- A Large and Diverse Dataset for Event-Based Depth Estimation

TL;DR

This paper introduces EvtSlowTV, a large-scale, diverse event camera dataset from YouTube footage, enabling improved self-supervised depth estimation in challenging environments without frame-based annotations.

Contribution

The creation of EvtSlowTV, the largest event dataset to date, and its application for self-supervised depth learning, enhancing generalization to complex scenes.

Findings

EvtSlowTV contains over 13 billion events from diverse scenarios.

Training on EvtSlowTV improves depth estimation in complex environments.

The dataset enables self-supervised learning without frame annotations.

Abstract

Event cameras, with their high dynamic range (HDR) and low latency, offer a promising alternative for robust depth estimation in challenging environments. However, many event-based depth estimation approaches are constrained by small-scale annotated datasets, limiting their generalizability to real-world scenarios. To bridge this gap, we introduce EvtSlowTV, a large-scale event camera dataset curated from publicly available YouTube footage, which contains more than 13B events across various environmental conditions and motions, including seasonal hiking, flying, scenic driving, and underwater exploration. EvtSlowTV is an order of magnitude larger than existing event datasets, providing an unconstrained, naturalistic setting for event-based depth learning. This work shows the suitability of EvtSlowTV for a self-supervised learning framework to capitalise on the HDR potential of raw event streams. We further demonstrate that training with EvtSlowTV enhances the model's ability to generalise to complex scenes and motions. Our approach removes the need for frame-based annotations and preserves the asynchronous nature of event data.