# GraftNet: Towards Domain Generalized Stereo Matching with a Broad-Spectrum and Task-Oriented Feature

arXiv: https://arxiv.org/abs/2204.00179 · HTML: https://mlsanity.com/paper/2204.00179/

## Key facts

- Cited by 43 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 1 Apr 2022.

## Builds on (most cited first, 30 of 32)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [Dense Contrastive Learning for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2011.09157/) (2020)
- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [End-to-End Learning of Geometry and Context for Deep Stereo Regression](https://mlsanity.com/paper/1703.04309/) (2017)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching](https://mlsanity.com/paper/2109.07547/) (2021)
- [GA-Net: Guided Aggregation Net for End-to-end Stereo Matching](https://mlsanity.com/paper/1904.06587/) (2019)
- [Group-wise Correlation Stereo Network](https://mlsanity.com/paper/1903.04025/) (2019)
- [AANet: Adaptive Aggregation Network for Efficient Stereo Matching](https://mlsanity.com/paper/2004.09548/) (2020)
- [Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2011.02910/) (2020)
- [Hierarchical Neural Architecture Search for Deep Stereo Matching](https://mlsanity.com/paper/2010.13501/) (2020)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Domain-invariant Stereo Matching Networks](https://mlsanity.com/paper/1911.13287/) (2019)
- [SegStereo: Exploiting Semantic Information for Disparity Estimation](https://mlsanity.com/paper/1807.11699/) (2018)
- [Real-time self-adaptive deep stereo](https://mlsanity.com/paper/1810.05424/) (2018)
- [Computing the Stereo Matching Cost with a Convolutional Neural Network](https://mlsanity.com/paper/1409.4326/) (2014)
- [Knowledge Distillation for Multi-task Learning](https://mlsanity.com/paper/2007.06889/) (2020)
- [Learning Stereo from Single Images](https://mlsanity.com/paper/2008.01484/) (2020)
- [Learning to Adapt for Stereo](https://mlsanity.com/paper/1904.02957/) (2019)
- [EdgeStereo: An Effective Multi-Task Learning Network for Stereo Matching and Edge Detection](https://mlsanity.com/paper/1903.01700/) (2019)
- [Matching-space Stereo Networks for Cross-domain Generalization](https://mlsanity.com/paper/2010.07347/) (2020)
- [AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching](https://mlsanity.com/paper/2004.04627/) (2020)
- [Guided Stereo Matching](https://mlsanity.com/paper/1905.10107/) (2019)
- [StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching](https://mlsanity.com/paper/2005.01927/) (2020)
- [Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching](https://mlsanity.com/paper/1806.01677/) (2018)

## Built on by (most cited first, 30 of 43)

- [CAT-Seg: Cost Aggregation for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2303.11797/) (2023)
- [Iterative Geometry Encoding Volume for Stereo Matching](https://mlsanity.com/paper/2303.06615/) (2023)
- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching](https://mlsanity.com/paper/2409.00638/) (2024)
- [Stereo Anywhere: Robust Zero-Shot Deep Stereo Matching Even Where Either Stereo or Mono Fail](https://mlsanity.com/paper/2412.04472/) (2024)
- [NeRF-Supervised Deep Stereo](https://mlsanity.com/paper/2303.17603/) (2023)
- [Booster: a Benchmark for Depth from Images of Specular and Transparent Surfaces](https://mlsanity.com/paper/2301.08245/) (2023)
- [Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching](https://mlsanity.com/paper/2512.11130/) (2025)
- [A Survey on Deep Stereo Matching in the Twenties](https://mlsanity.com/paper/2407.07816/) (2024)
- [Adaptive Multi-Modal Cross-Entropy Loss for Stereo Matching](https://mlsanity.com/paper/2306.15612/) (2023)
- [Revisiting Depth Completion from a Stereo Matching Perspective for Cross-domain Generalization](https://mlsanity.com/paper/2312.09254/) (2023)
- [CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction](https://mlsanity.com/paper/2301.02789/) (2023)
- [Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence](https://mlsanity.com/paper/2403.11120/) (2024)
- [Learning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts](https://mlsanity.com/paper/2507.04631/) (2025)
- [Federated Online Adaptation for Deep Stereo](https://mlsanity.com/paper/2405.14873/) (2024)
- [Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking](https://mlsanity.com/paper/2505.23495/) (2025)
- [Learning Intra-view and Cross-view Geometric Knowledge for Stereo Matching](https://mlsanity.com/paper/2402.19270/) (2024)
- [Diving into the Fusion of Monocular Priors for Generalized Stereo Matching](https://mlsanity.com/paper/2505.14414/) (2025)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching](https://mlsanity.com/paper/2409.01782/) (2024)
- [Stereo Risk: A Continuous Modeling Approach to Stereo Matching](https://mlsanity.com/paper/2407.03152/) (2024)
- [PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts](https://mlsanity.com/paper/2603.01650/) (2026)
- [These Maps Are Made by Propagation: Adapting Deep Stereo Networks to Road Scenarios with Decisive Disparity Diffusion](https://mlsanity.com/paper/2411.03717/) (2024)
- [Stereo Anything: Unifying Zero-shot Stereo Matching with Large-Scale Mixed Data](https://mlsanity.com/paper/2411.14053/) (2024)
- [StereoFlowGAN: Co-training for Stereo and Flow with Unsupervised Domain Adaptation](https://mlsanity.com/paper/2309.01842/) (2023)
- [DCVSMNet: Double Cost Volume Stereo Matching Network](https://mlsanity.com/paper/2402.16473/) (2024)
- [OV-COAST: Cost Aggregation with Optimal Transport for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2506.03706/) (2025)
- [PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models](https://mlsanity.com/paper/2507.08400/) (2025)
- [SR-Stereo & DAPE: Stepwise Regression and Pre-trained Edges for Practical Stereo Matching](https://mlsanity.com/paper/2406.06953/) (2024)
- [Towards Generalizable Referring Image Segmentation via Target Prompt and Visual Coherence](https://mlsanity.com/paper/2312.00452/) (2023)

## Abstract (arXiv)

Although supervised deep stereo matching networks have made impressive achievements, the poor generalization ability caused by the domain gap prevents them from being applied to real-life scenarios. In this paper, we propose to leverage the feature of a model trained on large-scale datasets to deal with the domain shift since it has seen various styles of images. With the cosine similarity based cost volume as a bridge, the feature will be grafted to an ordinary cost aggregation module. Despite the broad-spectrum representation, such a low-level feature contains much general information which is not aimed at stereo matching. To recover more task-specific information, the grafted feature is further input into a shallow network to be transformed before calculating the cost. Extensive experiments show that the model generalization ability can be improved significantly with this broad-spectrum and task-oriented feature. Specifically, based on two well-known architectures PSMNet and GANet, our methods are superior to other robust algorithms when transferring from SceneFlow to KITTI 2015, KITTI 2012, and Middlebury. Code is available at https://github.com/SpadeLiu/Graft-PSMNet.