URNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation
This work addresses depth estimation for event cameras, which is important for robotics and autonomous systems, but appears incremental as it builds on existing event-based stereo methods.
The paper tackles event-based stereo depth estimation by introducing URNet, an uncertainty-aware refinement network that incorporates local-global refinement and KL divergence-based uncertainty modeling, achieving state-of-the-art performance on the DSEC dataset.
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.