mlsanityData to 13 Oct 2026

StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation

arXiv 2309.01842 ↗First posted 4 Sep 2023cs.CVcited by 4

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Abstract (arXiv)

We introduce a novel training strategy for stereo matching and optical flow estimation that utilizes image-to-image translation between synthetic and real image domains. Our approach enables the training of models that excel in real image scenarios while relying solely on ground-truth information from synthetic images. To facilitate task-agnostic domain adaptation and the training of task-specific components, we introduce a bidirectional feature warping module that handles both left-right and forward-backward directions. Experimental results show competitive performance over previous domain translation-based methods, which substantiate the efficacy of our proposed framework, effectively leveraging the benefits of unsupervised domain adaptation, stereo matching, and optical flow estimation.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 41 of its 51 references that are in the map. First posted on arXiv 4 Sep 2023.

Builds on

41 of 51 references.

References · 41

  1. 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,516
  2. 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,395
  3. 2017Adversarial Discriminative Domain Adaptationcited by 1,310
  4. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,057
  5. 2017PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volumecited by 967
  6. 2015A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimationcited by 879
  7. 2016FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networkscited by 782
  8. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  9. 2022Continual Test-Time Domain Adaptationcited by 523
  10. 2018Pyramid Stereo Matching Networkcited by 489
  11. 2019Contrastive Adaptation Network for Unsupervised Domain Adaptationcited by 407
  12. 2021RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matchingcited by 352
29 more
  1. 2020Virtual KITTI 2cited by 349
  2. 2021GMFlow: Learning Optical Flow via Global Matchingcited by 326
  3. 2022Unifying Flow, Stereo and Depth Estimationcited by 271
  4. 2020AANet: Adaptive Aggregation Network for Efficient Stereo Matchingcited by 202
  5. 2022Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlationcited by 197
  6. 2023Iterative Geometry Encoding Volume for Stereo Matchingcited by 189
  7. 2021CFNet: Cascade and Fused Cost Volume for Robust Stereo Matchingcited by 154
  8. 2015Stereo Matching by Training a Convolutional Neural Network to Compare Image Patchescited by 151
  9. 2020Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimationcited by 120
  10. 2020HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matchingcited by 120
  11. 2022Source-Free Domain Adaptation via Distribution Estimationcited by 97
  12. 2017SegFlow: Joint Learning for Video Object Segmentation and Optical Flowcited by 92
  13. 2021MobileStereoNet: Towards Lightweight Deep Networks for Stereo Matchingcited by 82
  14. 2018Real-time self-adaptive deep stereocited by 80
  15. 2019A Lightweight Optical Flow CNN - Revisiting Data Fidelity and Regularizationcited by 76
  16. 2021Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentationcited by 76
  17. 2021Category Contrast for Unsupervised Domain Adaptation in Visual Taskscited by 65
  18. 2020UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learningcited by 49
  19. 2022GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Featurecited by 43
  20. 2019Learning to Adapt for Stereocited by 42
  21. 2022ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networkscited by 39
  22. 2020Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matchingcited by 36
  23. 2020AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matchingcited by 31
  24. 2021GAIA: A Transfer Learning System of Object Detection that Fits Your Needscited by 27
  25. 2019Bridging Stereo Matching and Optical Flow via Spatiotemporal Correspondencecited by 23
  26. 2020StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matchingcited by 21
  27. 2018Zoom and Learn: Generalizing Deep Stereo Matching to Novel Domainscited by 20
  28. 2021SpotPatch: Parameter-Efficient Transfer Learning for Mobile Object Detectioncited by 3
  29. 2018Joint Coarse-And-Fine Reasoning for Deep Optical Flowcited by 1

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2025Towards Open-World Generation of Stereo Images and Unsupervised Matchingcited by 5
  2. 2024Investigation of Hierarchical Spectral Vision Transformer Architecture for Classification of Hyperspectral Imagerycited by 2
  3. 2023FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimationcited by 1
  4. 2026GenOpticalFlow: A Generative Approach to Unsupervised Optical Flow Learningcited by 0