U^2Flow：不确定性感知的无监督光流估计
U^2Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation
把增强一致性通过拉普拉斯最大似然转成逐像素不确定性监督，可在无真值场景下为任何光流或匹配网络提供可用的置信度，并用于引导细化与区域平滑权重。
原文依据 derives uncertainty supervision from augmentation consistency via a Laplace-based maximum likelihood objective
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论无监督光流方法通常缺乏可靠的不确定性估计，这限制了其鲁棒性与可解释性。本文提出 U^2Flow，这是首个联合估计光流与逐像素不确定性的循环无监督框架。其核心创新是一种解耦学习策略：通过基于拉普拉斯的最大似然目标，从增强一致性中导出不确定性监督，从而在无真值的情况下实现稳定训练。预测的不确定性进一步被引入网络，用于引导自适应光流细化，并动态调制区域平滑损失。此外，本文提出一种不确定性引导的双向光流融合机制，增强了在困难区域中的鲁棒性。在 KITTI 和 Sintel 上的大量实验表明，U^2Flow 在无监督方法中取得了最优性能，同时生成高度可靠的不确定性图，验证了联合估计范式的有效性。代码见 https://github.com/sunzunyi/U2FLOW。
英文原文摘要 1069 字符
Unsupervised optical flow methods typically lack reliable uncertainty estimation, limiting their robustness and interpretability. We propose U^ 2 Flow, the first recurrent unsupervised framework that jointly estimates optical flow and per-pixel uncertainty. The core innovation is a decoupled learning strategy that derives uncertainty supervision from augmentation consistency via a Laplace-based maximum likelihood objective, enabling stable training without ground truth. The predicted uncertainty is further integrated into the network to guide adaptive flow refinement and dynamically modulate the regional smoothness loss. Furthermore, we introduce an uncertainty-guided bidirectional flow fusion mechanism that enhances robustness in challenging regions. Extensive experiments on KITTI and Sintel demonstrate that U^ 2 Flow achieves state-of-the-art performance among unsupervised methods while producing highly reliable uncertainty maps, validating the effectiveness of our joint estimation paradigm. The code is available at https://github.com/sunzunyi/U2FLOW.