U$^{2}$Flow: Uncertainty-Aware Unsupervised Optical Flow Estimation

2026-04-11

AI Summary

This work addresses the lack of reliable pixel-wise uncertainty estimation in unsupervised optical flow methods, which limits their robustness and interpretability. The authors propose a recurrent unsupervised framework that, for the first time, incorporates a decoupled learning strategy to jointly estimate optical flow and its per-pixel uncertainty. By formulating a maximum likelihood objective based on the Laplace distribution and integrating data augmentation consistency, the model enables stable training without ground-truth supervision. Furthermore, an uncertainty-guided bidirectional flow fusion mechanism and an adaptive regional smoothness loss are introduced to enhance performance in challenging regions. The method achieves state-of-the-art results among unsupervised approaches on both KITTI and Sintel benchmarks and produces highly reliable uncertainty maps.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Sequential Decision Making
• Computer Vision: Learning & Optimization for CV

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• unsupervised optical flow
• uncertainty estimation
• robustness
• interpretability

Innovation

• Uncertainty Estimation
• Unsupervised Optical Flow
• Augmentation Consistency
• Adaptive Refinement
• Bidirectional Flow Fusion