@article{Cheng2025, 
author = {Yifeng Cheng and Alois Knoll and Hu Cao},
title = {URNet: uncertainty-aware refinement network for event-based stereo depth estimation},
year = {2025},
journal = {Visual Intelligence},
volume = {3},
pages = {18},
keywords = {Autonomous driving, Event camera, Uncertainty-aware refinement network (URNet), Stereo depth estimation},
url = {https://www.sciopen.com/article/10.1007/s44267-025-00090-1},
doi = {10.1007/s44267-025-00090-1},
abstract = {Event cameras provide high temporal resolution, high dynamic range, and low latency, offering significant advantages over conventional frame-based cameras. In this work, we introduce an uncertainty-aware refinement network called URNet for event-based stereo depth estimation. Our approach features a local-global refinement module that effectively captures fine-grained local details and long-range global context. Additionally, we introduce a Kullback-Leibler (KL) divergence-based uncertainty modeling method to enhance prediction reliability. Extensive experiments on the DSEC dataset demonstrate that URNet consistently outperforms state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations.}
}