📝Awesome and classical image retrieval papers
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Updated
Oct 31, 2023
📝Awesome and classical image retrieval papers
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features
My personal note about local and global descriptor
Implementation of XFeat (CVPR 2024). Do you need robust and fast local feature extraction? You are in the right place!
Joint Deep Matcher for Points and Lines 🖼️💥🖼️ (ICCV 2023)
Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"
PyTorch Implementation of "Large-Scale Image Retrieval with Attentive Deep Local Features"
Open Source Graph Neural Net Based Pipeline for Image Matching
ONNX-compatible LightGlue: Local Feature Matching at Light Speed. Supports TensorRT, OpenVINO
Code and weights for local feature affine shape estimation paper "Repeatability Is Not Enough: Learning Discriminative Affine Regions via Discriminability"
ELSED: Enhanced Line SEgment Drawing
Baselines for the Image Matching Benchmark and Challenge
Comparative Evaluation of Hand-Crafted and Learned Local Features
PyTorch implementation of SIFT descriptor
🚀🚀 Revisiting Binary Local Image Description for Resource Limited Devices
[CVPR2022] Decoupling Makes Weakly Supervised Local Feature Better
[CVPR 2023] SFD2: Semantic-guided Feature Detection and Description. Embedding semantics into local features implicitly for long-term visual localization
HOW local descriptors
MODS (Matching On Demand with view Synthesis) is algorithm for wide-baseline matching.
[ECCV 2020] Single image depth prediction allows us to rectify planar surfaces in images and extract view-invariant local features for better feature matching
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