U$^{2}$Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation
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...
| Authors | Xunpei Sun, Wenwei Lin, Yi Chang et al. |
|---|---|
| Primary category | cs.CV |
| Published | 11 April 2026 |
| Last updated at source | 11 April 2026 |
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