# SIFT Matching by Context Exposed arXiv: https://arxiv.org/abs/2106.09584 ยท HTML: https://mlsanity.com/paper/2106.09584/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 96 references that are in the map. - First posted on arXiv 17 Jun 2021. ## Builds on (most cited first, 30 of 30) - [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) - [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) - [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) - [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017) - [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016) - [Image Matching across Wide Baselines: From Paper to Practice](https://mlsanity.com/paper/2003.01587/) (2020) - [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017) - [Learning to Find Good Correspondences](https://mlsanity.com/paper/1711.05971/) (2017) - [LF-Net: Learning Local Features from Images](https://mlsanity.com/paper/1805.09662/) (2018) - [Learning Two-View Correspondences and Geometry Using Order-Aware Network](https://mlsanity.com/paper/1908.04964/) (2019) - [Graph-Cut RANSAC](https://mlsanity.com/paper/1706.00984/) (2017) - [SOSNet: Second Order Similarity Regularization for Local Descriptor Learning](https://mlsanity.com/paper/1904.05019/) (2019) - [Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses](https://mlsanity.com/paper/1905.04132/) (2019) - [Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters](https://mlsanity.com/paper/1904.00889/) (2019) - [ContextDesc: Local Descriptor Augmentation with Cross-Modality Context](https://mlsanity.com/paper/1904.04084/) (2019) - [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://mlsanity.com/paper/1608.01807/) (2016) - [Repeatability Is Not Enough: Learning Affine Regions via Discriminability](https://mlsanity.com/paper/1711.06704/) (2017) - [GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints](https://mlsanity.com/paper/1807.06294/) (2018) - [Progressive-X: Efficient, Anytime, Multi-Model Fitting Algorithm](https://mlsanity.com/paper/1906.02290/) (2019) - [D2D: Keypoint Extraction with Describe to Detect Approach](https://mlsanity.com/paper/2005.13605/) (2020) - [NM-Net: Mining Reliable Neighbors for Robust Feature Correspondences](https://mlsanity.com/paper/1904.00320/) (2019) - [MODS: Fast and Robust Method for Two-View Matching](https://mlsanity.com/paper/1503.02619/) (2015) - [An Evaluation of Feature Matchers for Fundamental Matrix Estimation](https://mlsanity.com/paper/1908.09474/) (2019) - [Learning to Assign Orientations to Feature Points](https://mlsanity.com/paper/1511.04273/) (2015) - [Domain-Size Pooling in Local Descriptors: DSP-SIFT](https://mlsanity.com/paper/1412.8556/) (2014) - [Leveraging Outdoor Webcams for Local Descriptor Learning](https://mlsanity.com/paper/1901.09780/) (2019) ## Built on by (most cited first, 9 of 9) - [Relation Preserving Triplet Mining for Stabilising the Triplet Loss in Re-identification Systems](https://mlsanity.com/paper/2110.07933/) (2021) - [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023) - [HarrisZ^+: Harris Corner Selection for Next-Gen Image Matching Pipelines](https://mlsanity.com/paper/2109.12925/) (2021) - [Image Matching by Bare Homography](https://mlsanity.com/paper/2305.08946/) (2023) - [Image Matching Filtering and Refinement by Planes and Beyond](https://mlsanity.com/paper/2411.09484/) (2024) - [Region Matters: Efficient and Reliable Region-Aware Visual Place Recognition](https://mlsanity.com/paper/2604.22390/) (2026) - [Underwater target detection based on improved YOLOv7](https://mlsanity.com/paper/2302.06939/) (2023) - [Mask wearing object detection algorithm based on improved YOLOv5](https://mlsanity.com/paper/2310.10245/) (2023) - [SoundLoc3D: Invisible 3D Sound Source Localization and Classification Using a Multimodal RGB-D Acoustic Camera](https://mlsanity.com/paper/2412.16861/) (2024) ## Abstract (arXiv) This paper investigates how to step up local image descriptor matching by exploiting matching context information. Two main contexts are identified, originated respectively from the descriptor space and from the keypoint space. The former is generally used to design the actual matching strategy while the latter to filter matches according to the local spatial consistency. On this basis, a new matching strategy and a novel local spatial filter, named respectively blob matching and Delaunay Triangulation Matching (DTM) are devised. Blob matching provides a general matching framework by merging together several strategies, including rank-based pre-filtering as well as many-to-many and symmetric matching, enabling to achieve a global improvement upon each individual strategy. DTM alternates between Delaunay triangulation contractions and expansions to figure out and adjust keypoint neighborhood consistency. Experimental evaluation shows that DTM is comparable or better than the state-of-the-art in terms of matching accuracy and robustness. Evaluation is carried out according to a new benchmark devised for analyzing the matching pipeline in terms of correct correspondences on both planar and non-planar scenes, including several state-of-the-art methods as well as the common SIFT matching approach for reference. This evaluation can be of assistance for future research in this field.