用不确定性建模提升事件相机立体深度估计精度
URNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation
- 设计局部全局融合模块捕捉细节与上下文
- 基于KL散度建模不确定性，提升预测可靠性
- 在DSEC数据集上超越现有最优方法
事件相机具有高时间分辨率、高动态范围和低延迟的优势，优于传统帧式相机。本文提出一种不确定性感知的精炼网络URNet，用于事件相机立体深度估计。方法包含一个局部-全局精炼模块，可有效捕捉细粒度局部细节与长距离全局上下文；同时引入基于KL散度的不确定性建模方法，提升预测可靠性。在DSEC数据集上的大量实验表明，URNet在定性和定量评估中均持续优于当前最先进方法。
原文摘要 · Abstract (English)
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.
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