# DEFOM-Stereo: Depth Foundation Model Based Stereo Matching

arXiv: https://arxiv.org/abs/2501.09466 · HTML: https://mlsanity.com/paper/2501.09466/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 49 of its 71 references that are in the map.
- First posted on arXiv 16 Jan 2025.

## Builds on (most cited first, 30 of 49)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (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)
- [Unifying Flow, Stereo and Depth Estimation](https://mlsanity.com/paper/2211.05783/) (2022)
- [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)
- [Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation](https://mlsanity.com/paper/2203.11483/) (2022)
- [Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2011.02910/) (2020)
- [Iterative Geometry Encoding Volume for Stereo Matching](https://mlsanity.com/paper/2303.06615/) (2023)
- [Probing the 3D Awareness of Visual Foundation Models](https://mlsanity.com/paper/2404.08636/) (2024)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [Attention Concatenation Volume for Accurate and Efficient Stereo Matching](https://mlsanity.com/paper/2203.02146/) (2022)
- [3D Ken Burns Effect from a Single Image](https://mlsanity.com/paper/1909.05483/) (2019)
- [Hierarchical Deep Stereo Matching on High-resolution Images](https://mlsanity.com/paper/1912.06704/) (2019)
- [HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching](https://mlsanity.com/paper/2007.12140/) (2020)
- [Domain-invariant Stereo Matching Networks](https://mlsanity.com/paper/1911.13287/) (2019)
- [Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching](https://mlsanity.com/paper/2403.00486/) (2024)
- [Falling Things: A Synthetic Dataset for 3D Object Detection and Pose Estimation](https://mlsanity.com/paper/1804.06534/) (2018)

## Built on by (most cited first, 30 of 52)

- [Fast-FoundationStereo: Real-Time Zero-Shot Stereo Matching](https://mlsanity.com/paper/2512.11130/) (2025)
- [BridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment](https://mlsanity.com/paper/2508.04611/) (2025)
- [BANet: Bilateral Aggregation Network for Mobile Stereo Matching](https://mlsanity.com/paper/2503.03259/) (2025)
- [DuCos: Duality Constrained Depth Super-Resolution via Foundation Model](https://mlsanity.com/paper/2503.04171/) (2025)
- [FlowSeek: Optical Flow Made Easier with Depth Foundation Models and Motion Bases](https://mlsanity.com/paper/2509.05297/) (2025)
- [StereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes](https://mlsanity.com/paper/2509.16415/) (2025)
- [Stereo Any Video: Temporally Consistent Stereo Matching](https://mlsanity.com/paper/2503.05549/) (2025)
- [PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts](https://mlsanity.com/paper/2603.01650/) (2026)
- [Lite Any Stereo: Efficient Zero-Shot Stereo Matching](https://mlsanity.com/paper/2511.16555/) (2025)
- [Large Depth Completion Model from Sparse Observations](https://mlsanity.com/paper/2605.30115/) (2026)
- [NTIRE 2025 Challenge on HR Depth from Images of Specular and Transparent Surfaces](https://mlsanity.com/paper/2506.05815/) (2025)
- [DepthFocus: Controllable Depth Estimation for See-Through Scenes](https://mlsanity.com/paper/2511.16993/) (2025)
- [Towards Gold-Standard Depth Estimation for Tree Branches in UAV Forestry: Benchmarking Deep Stereo Matching Methods](https://mlsanity.com/paper/2601.19461/) (2026)
- [WAFT-Stereo: Warping-Alone Field Transforms for Stereo Matching](https://mlsanity.com/paper/2603.24836/) (2026)
- [A Wavelet-based Stereo Matching Framework for Solving Frequency Convergence Inconsistency](https://mlsanity.com/paper/2505.18024/) (2025)
- [PanMatch: Unleashing the Potential of Large Vision Models for Unified Matching Models](https://mlsanity.com/paper/2507.08400/) (2025)
- [Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation](https://mlsanity.com/paper/2507.11540/) (2025)
- [SMFormer: Empowering Self-supervised Stereo Matching via Foundation Models and Data Augmentation](https://mlsanity.com/paper/2604.10218/) (2026)
- [NeoARCADE: Robust Calibration for Distance Estimation to Support Assistive Drones for the Visually Impaired](https://mlsanity.com/paper/2504.01988/) (2025)
- [SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams](https://mlsanity.com/paper/2505.19487/) (2025)
- [CogStereo: Neural Stereo Matching with Implicit Spatial Cognition Embedding](https://mlsanity.com/paper/2510.22119/) (2025)
- [StereoVGGT: A Training-Free Visual Geometry Transformer for Stereo Vision](https://mlsanity.com/paper/2603.29368/) (2026)
- [The Role of Cyclopean-Eye in Stereo Vision](https://mlsanity.com/paper/2506.20900/) (2025)
- [Stextsuperscript2Mtextsuperscript2: Scalable Stereo Matching Model for Reliable Depth Estimation](https://mlsanity.com/paper/2507.13229/) (2025)
- [MAGIC: Learning from Visibility Asymmetry for Unsupervised Stereo Matching](https://mlsanity.com/paper/2508.10838/) (2025)
- [Towards Understanding 3D Vision: the Role of Gaussian Curvature](https://mlsanity.com/paper/2508.11825/) (2025)
- [MatchAttention: Embedding Explicit Matching Constraints into Attention for Efficient Stereo Matching](https://mlsanity.com/paper/2510.14260/) (2025)
- [MODEST: Multi-Optics Depth-of-Field Stereo Dataset](https://mlsanity.com/paper/2511.20853/) (2025)
- [Neural reconstruction of 3D ocean wave hydrodynamics from camera sensing](https://mlsanity.com/paper/2512.06024/) (2025)
- [Generalized Geometry Encoding Volume for Real-time Stereo Matching](https://mlsanity.com/paper/2512.06793/) (2025)

## Abstract (arXiv)

Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estimation from binocular matching cues. Recently, monocular relative depth estimation has shown remarkable generalization using vision foundation models. Thus, to facilitate robust stereo matching with monocular depth cues, we incorporate a robust monocular relative depth model into the recurrent stereo-matching framework, building a new framework for depth foundation model-based stereo-matching, DEFOM-Stereo. In the feature extraction stage, we construct the combined context and matching feature encoder by integrating features from conventional CNNs and DEFOM. In the update stage, we use the depth predicted by DEFOM to initialize the recurrent disparity and introduce a scale update module to refine the disparity at the correct scale. DEFOM-Stereo is verified to have much stronger zero-shot generalization compared with SOTA methods. Moreover, DEFOM-Stereo achieves top performance on the KITTI 2012, KITTI 2015, Middlebury, and ETH3D benchmarks, ranking 1st on many metrics. In the joint evaluation under the robust vision challenge, our model simultaneously outperforms previous models on the individual benchmarks, further demonstrating its outstanding capabilities.