# Deep Learning Reforms Image Matching: A Survey and Outlook

arXiv: https://arxiv.org/abs/2506.04619 · HTML: https://mlsanity.com/paper/2506.04619/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 217 of its 387 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 30 of 217)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://mlsanity.com/paper/2104.09864/) (2021)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention](https://mlsanity.com/paper/2006.16236/) (2020)
- [Vision Transformers for Dense Prediction](https://mlsanity.com/paper/2103.13413/) (2021)
- [DUSt3R: Geometric 3D Vision Made Easy](https://mlsanity.com/paper/2312.14132/) (2023)
- [VGGT: Visual Geometry Grounded Transformer](https://mlsanity.com/paper/2503.11651/) (2025)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [Spatial Transformer Networks](https://mlsanity.com/paper/1506.02025/) (2015)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [On the Continuity of Rotation Representations in Neural Networks](https://mlsanity.com/paper/1812.07035/) (2018)
- [Group Equivariant Convolutional Networks](https://mlsanity.com/paper/1602.07576/) (2016)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)

## Built on by (most cited first, 7 of 7)

- [Make Geometry Matter for Spatial Reasoning](https://mlsanity.com/paper/2603.26639/) (2026)
- [DistillMatch: Leveraging Knowledge Distillation from Vision Foundation Model for Multimodal Image Matching](https://mlsanity.com/paper/2509.16017/) (2025)
- [SceneGlue: Scene-Aware Transformer for Feature Matching without Scene-Level Annotation](https://mlsanity.com/paper/2604.13941/) (2026)
- [SPIDER: Spatial Image CorresponDence Estimator for Robust Calibration](https://mlsanity.com/paper/2511.17750/) (2025)
- [Matching of SAR and optical images based on transformation to shared modality](https://mlsanity.com/paper/2602.12515/) (2026)
- [MatRes: Zero-Shot Test-Time Model Adaptation for Simultaneous Matching and Restoration](https://mlsanity.com/paper/2604.10081/) (2026)
- [Misanthrope: A Privacy-Preserving Keypoint Detector](https://mlsanity.com/paper/2608.23012/) (2026)

## Abstract (arXiv)

Image matching, which establishes correspondences between two images to recover 3D structure and camera geometry, is a cornerstone of computer vision and underpins a wide range of applications, including visual localization, 3D reconstruction, and simultaneous localization and mapping (SLAM). Traditional pipelines, composed of a detector-descriptor, a feature matcher, an outlier filter, and a geometric estimator, falter in challenging scenarios. Recent advances in deep learning have substantially improved both their robustness and accuracy. This survey reviews how deep learning has progressively transformed the classical image matching pipeline. Our taxonomy is aligned with the traditional pipeline and covers two aspects: i) replacing individual steps with learnable alternatives, including learnable detector-descriptors, outlier filters, and geometric estimators; and ii) merging multiple steps into end-to-end learnable modules, including middle-end sparse matchers, end-to-end semi-dense/dense matchers, and pose regressors. We first examine the design principles, advantages, and limitations of both aspects, and then benchmark representative methods on relative pose estimation, homography estimation, matching accuracy assessment, visual localization, and 3D reconstruction. Finally, we discuss open challenges and directions for future research. By systematically categorizing and evaluating learning-based methods, this survey offers a clear overview of how image matching is evolving and where further progress is needed. The project repository is available at https://github.com/ZizhuoLi/awesome-image-matching-survey.