# Rethinking Unsupervised Cross-modal Flow Estimation: Learning from Decoupled Optimization and Consistency Constraint

arXiv: https://arxiv.org/abs/2509.24423 · HTML: https://mlsanity.com/paper/2509.24423/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 36 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 28 of 28)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [UniDepth: Universal Monocular Metric Depth Estimation](https://mlsanity.com/paper/2403.18913/) (2024)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [Learning to Estimate Hidden Motions with Global Motion Aggregation](https://mlsanity.com/paper/2104.02409/) (2021)
- [SEA-RAFT: Simple, Efficient, Accurate RAFT for Optical Flow](https://mlsanity.com/paper/2405.14793/) (2024)
- [Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation](https://mlsanity.com/paper/2003.13045/) (2020)
- [Flash3D: Feed-Forward Generalisable 3D Scene Reconstruction from a Single Image](https://mlsanity.com/paper/2406.04343/) (2024)
- [Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness](https://mlsanity.com/paper/1608.05842/) (2016)
- [Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration](https://mlsanity.com/paper/2205.11876/) (2022)
- [Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation](https://mlsanity.com/paper/2003.08073/) (2020)
- [UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning](https://mlsanity.com/paper/2012.00212/) (2020)
- [MINIMA: Modality Invariant Image Matching](https://mlsanity.com/paper/2412.19412/) (2024)
- [Learning Stereo from Single Images](https://mlsanity.com/paper/2008.01484/) (2020)
- [DCEvo: Discriminative Cross-Dimensional Evolutionary Learning for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2503.17673/) (2025)
- [From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion](https://mlsanity.com/paper/2401.00421/) (2023)
- [Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow](https://mlsanity.com/paper/2303.07564/) (2023)
- [Flow-Anything: Learning Real-World Optical Flow Estimation from Large-Scale Single-view Images](https://mlsanity.com/paper/2506.07740/) (2025)
- [UnSAMFlow: Unsupervised Optical Flow Guided by Segment Anything Model](https://mlsanity.com/paper/2405.02608/) (2024)
- [MPI-Flow: Learning Realistic Optical Flow with Multiplane Images](https://mlsanity.com/paper/2309.06714/) (2023)
- [SCPNet: Unsupervised Cross-modal Homography Estimation via Intra-modal Self-supervised Learning](https://mlsanity.com/paper/2407.08148/) (2024)
- [Pixel-aligned RGB-NIR Stereo Imaging and Dataset for Robot Vision](https://mlsanity.com/paper/2411.18025/) (2024)
- [Towards Robust Image Stitching: An Adaptive Resistance Learning against Compatible Attacks](https://mlsanity.com/paper/2402.15959/) (2024)
- [SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split Optimization](https://mlsanity.com/paper/2409.17993/) (2024)
- [Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow](https://mlsanity.com/paper/2303.13761/) (2023)
- [SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising](https://mlsanity.com/paper/2404.00349/) (2024)

## Built on by (most cited first, 1 of 1)

- [Modality-Invariant Coarse-to-Fine Retinal Image Registration](https://mlsanity.com/paper/2608.14829/) (2026)

## Abstract (arXiv)

This work presents DCFlow, a novel unsupervised cross-modal flow estimation framework that integrates a decoupled optimization strategy and a cross-modal consistency constraint. Unlike previous approaches that implicitly learn flow estimation solely from appearance similarity, we introduce a decoupled optimization strategy with task-specific supervision to address modality discrepancy and geometric misalignment distinctly. This is achieved by collaboratively training a modality transfer network and a flow estimation network. To enable reliable motion supervision without ground-truth flow, we propose a geometry-aware data synthesis pipeline combined with an outlier-robust loss. Additionally, we introduce a cross-modal consistency constraint to jointly optimize both networks, significantly improving flow prediction accuracy. For evaluation, we construct a comprehensive cross-modal flow benchmark by repurposing public datasets. Experimental results demonstrate that DCFlow can be integrated with various flow estimation networks and achieves state-of-the-art performance among unsupervised approaches.