# SAMatcher: Dense Co-Visibility Modeling via Cross-View Fusion for Scale-Imbalance Image Matching

arXiv: https://arxiv.org/abs/2606.03406 · HTML: https://mlsanity.com/paper/2606.03406/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 49 references that are in the map.
- First posted on arXiv 2 Jun 2026.

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

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [Swin Transformer V2: Scaling Up Capacity and Resolution](https://mlsanity.com/paper/2111.09883/) (2021)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [LoFTR: Detector-Free Local Feature Matching with Transformers](https://mlsanity.com/paper/2104.00680/) (2021)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [PointRend: Image Segmentation as Rendering](https://mlsanity.com/paper/1912.08193/) (2019)
- [Segment Anything in High Quality](https://mlsanity.com/paper/2306.01567/) (2023)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020)
- [Back to the Feature: Learning Robust Camera Localization from Pixels to Pose](https://mlsanity.com/paper/2103.09213/) (2021)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [MatchFormer: Interleaving Attention in Transformers for Feature Matching](https://mlsanity.com/paper/2203.09645/) (2022)
- [ContextDesc: Local Descriptor Augmentation with Cross-Modality Context](https://mlsanity.com/paper/1904.04084/) (2019)
- [GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints](https://mlsanity.com/paper/1807.06294/) (2018)
- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [Guide Local Feature Matching by Overlap Estimation](https://mlsanity.com/paper/2202.09050/) (2022)
- [Matchable Image Retrieval by Learning from Surface Reconstruction](https://mlsanity.com/paper/1811.10343/) (2018)
- [CoMatcher: Multi-View Collaborative Feature Matching](https://mlsanity.com/paper/2504.01872/) (2025)
- [ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses](https://mlsanity.com/paper/2410.22733/) (2024)

## Abstract (arXiv)

Reliable correspondence estimation supports image processing and 3D vision tasks, including Structure from Motion, visual localization, and image registration. Wide-baseline matching is difficult under cross-view scale imbalance. The same content may appear at different resolutions and spatial extents, creating unequal observation quality. Such pairs constitute low-quality multi-view data, even when each image is clear. Image information that is distinctive in one view may be weak or unavailable in the other. Existing methods rely on pixel- or patch-level appearance and do not explicitly identify where reliable evidence is shared across views. We propose SAMatcher, a modular co-visibility framework for scale-imbalanced image matching. It uses symmetric cross-view interaction to fuse the two feature representations and identify their shared spatial support. From the fused features, SAMatcher predicts dense co-visible masks and bounding boxes. These predictions define view-specific cropping windows for downstream point-level matching. Built upon the Segment Anything Model (SAM), SAMatcher extends monocular region modeling to cross-view co-visibility reasoning. Point-sampled mask learning, box regression, and mask--box consistency jointly supervise its multi-granularity predictions. Extensive experiments follow an evaluation protocol centered on cross-view scale imbalance. The results show that SAMatcher consistently improves matching robustness across diverse matching pipelines. When integrated with RoMa, SAMatcher improves AUC@5 and mAA@5 by 5.58 and 4.97 points, respectively. The results demonstrate that cross-view fusion and dense co-visibility modeling provide reliable priors for matching under scale-induced quality imbalance. Code and project page are available at https://xupan.top/projects/samatcher