# PATS: Patch Area Transportation with Subdivision for Local Feature Matching

arXiv: https://arxiv.org/abs/2303.07700 · HTML: https://mlsanity.com/paper/2303.07700/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- Builds on 52 of its 76 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 30 of 52)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [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)
- [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)
- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [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)
- [PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume](https://mlsanity.com/paper/1709.02371/) (2017)
- [A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation](https://mlsanity.com/paper/1512.02134/) (2015)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [Optimal Transport for Domain Adaptation](https://mlsanity.com/paper/1507.00504/) (2015)
- [D2-Net: A Trainable CNN for Joint Detection and Description of Local Features](https://mlsanity.com/paper/1905.03561/) (2019)
- [FlowFormer: A Transformer Architecture for Optical Flow](https://mlsanity.com/paper/2203.16194/) (2022)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [OTA: Optimal Transport Assignment for Object Detection](https://mlsanity.com/paper/2103.14259/) (2021)
- [Learning to Find Good Correspondences](https://mlsanity.com/paper/1711.05971/) (2017)
- [ASpanFormer: Detector-Free Image Matching with Adaptive Span Transformer](https://mlsanity.com/paper/2208.14201/) (2022)
- [Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation](https://mlsanity.com/paper/2210.15858/) (2022)
- [GLU-Net: Global-Local Universal Network for Dense Flow and Correspondences](https://mlsanity.com/paper/1912.05524/) (2019)
- [ASLFeat: Learning Local Features of Accurate Shape and Localization](https://mlsanity.com/paper/2003.10071/) (2020)
- [Patch2Pix: Epipolar-Guided Pixel-Level Correspondences](https://mlsanity.com/paper/2012.01909/) (2020)

## Built on by (most cited first, 30 of 33)

- [Grounding Image Matching in 3D with MASt3R](https://mlsanity.com/paper/2406.09756/) (2024)
- [RoMa: Robust Dense Feature Matching](https://mlsanity.com/paper/2305.15404/) (2023)
- [MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence](https://mlsanity.com/paper/2505.23764/) (2025)
- [Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed](https://mlsanity.com/paper/2403.04765/) (2024)
- [VLM-Grounder: A VLM Agent for Zero-Shot 3D Visual Grounding](https://mlsanity.com/paper/2410.13860/) (2024)
- [Multi-Modal Neural Radiance Field for Monocular Dense SLAM with a Light-Weight ToF Sensor](https://mlsanity.com/paper/2308.14383/) (2023)
- [Local Feature Matching Using Deep Learning: A Survey](https://mlsanity.com/paper/2401.17592/) (2024)
- [MESA: Matching Everything by Segmenting Anything](https://mlsanity.com/paper/2401.16741/) (2024)
- [iComMa: Inverting 3D Gaussian Splatting for Camera Pose Estimation via Comparing and Matching](https://mlsanity.com/paper/2312.09031/) (2023)
- [BlinkVision: A Benchmark for Optical Flow, Scene Flow and Point Tracking Estimation using RGB Frames and Events](https://mlsanity.com/paper/2410.20451/) (2024)
- [FAR: Flexible, Accurate and Robust 6DoF Relative Camera Pose Estimation](https://mlsanity.com/paper/2403.03221/) (2024)
- [CoMatch: Dynamic Covisibility-Aware Transformer for Bilateral Subpixel-Level Semi-Dense Image Matching](https://mlsanity.com/paper/2503.23925/) (2025)
- [Searching from Area to Point: A Hierarchical Framework for Semantic-Geometric Combined Feature Matching](https://mlsanity.com/paper/2305.00194/) (2023)
- [EDM: Efficient Deep Feature Matching](https://mlsanity.com/paper/2503.05122/) (2025)
- [Deep Learning Reforms Image Matching: A Survey and Outlook](https://mlsanity.com/paper/2506.04619/) (2025)
- [Affine steerers for structured keypoint description](https://mlsanity.com/paper/2408.14186/) (2024)
- [ETO:Efficient Transformer-based Local Feature Matching by Organizing Multiple Homography Hypotheses](https://mlsanity.com/paper/2410.22733/) (2024)
- [SRPose: Two-view Relative Pose Estimation with Sparse Keypoints](https://mlsanity.com/paper/2407.08199/) (2024)
- [PRISM: PRogressive dependency maxImization for Scale-invariant image Matching](https://mlsanity.com/paper/2408.03598/) (2024)
- [BlinkTrack: Feature Tracking over 80 FPS via Events and Images](https://mlsanity.com/paper/2409.17981/) (2024)
- [RGM: A Robust Generalizable Matching Model](https://mlsanity.com/paper/2310.11755/) (2023)
- [MESA: Effective Matching Redundancy Reduction by Semantic Area Segmentation](https://mlsanity.com/paper/2408.00279/) (2024)
- [A Global Depth-Range-Free Multi-View Stereo Transformer Network with Pose Embedding](https://mlsanity.com/paper/2411.01893/) (2024)
- [LGFCTR: Local and Global Feature Convolutional Transformer for Image Matching](https://mlsanity.com/paper/2311.17571/) (2023)
- [Are Semi-Dense Detector-Free Methods Good at Matching Local Features?](https://mlsanity.com/paper/2402.08671/) (2024)
- [CasP: Improving Semi-Dense Feature Matching Pipeline Leveraging Cascaded Correspondence Priors for Guidance](https://mlsanity.com/paper/2507.17312/) (2025)
- [SGAD: Semantic and Geometric-aware Descriptor for Local Feature Matching](https://mlsanity.com/paper/2508.02278/) (2025)
- [ChangingGrounding: 3D Visual Grounding in Changing Scenes](https://mlsanity.com/paper/2510.14965/) (2025)
- [Improving Local Feature Matching by Entropy-inspired Scale Adaptability and Flow-endowed Local Consistency](https://mlsanity.com/paper/2604.06713/) (2026)
- [DiffusionVS: A Generative Framework for Robust Visual Servoing Based on Diffusion Policy](https://mlsanity.com/paper/2606.19397/) (2026)

## Abstract (arXiv)

Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences. In this paper, we propose Patch Area Transportation with Subdivision (PATS) to tackle this issue. Instead of building an expensive image pyramid, we start by splitting the original image pair into equal-sized patches and gradually resizing and subdividing them into smaller patches with the same scale. However, estimating scale differences between these patches is non-trivial since the scale differences are determined by both relative camera poses and scene structures, and thus spatially varying over image pairs. Moreover, it is hard to obtain the ground truth for real scenes. To this end, we propose patch area transportation, which enables learning scale differences in a self-supervised manner. In contrast to bipartite graph matching, which only handles one-to-one matching, our patch area transportation can deal with many-to-many relationships. PATS improves both matching accuracy and coverage, and shows superior performance in downstream tasks, such as relative pose estimation, visual localization, and optical flow estimation. The source code is available at urlhttps://zju3dv.github.io/pats/.