U$^{2}$Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation
For computer vision applications requiring robust optical flow, this work provides a novel method to obtain uncertainty estimates without ground truth, improving interpretability and performance.
U$^{2}$Flow is the first recurrent unsupervised framework for joint optical flow and per-pixel uncertainty estimation, achieving state-of-the-art performance among unsupervised methods on KITTI and Sintel while producing reliable uncertainty maps.
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.