zju3dv/MatchAnything

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1279 stars · 41 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

MatchAnything is a deep learning model for universal cross-modality image matching, released as research code accompanying a TPAMI 2026 paper. It provides pre-trained weights via HuggingFace for matching keypoints across images from different modalities (e.g., sketches, paintings, different sensors).

Use cases

• match keypoints between images from different modalities
• find corresponding points across sketch and photo
• image matching for 3d reconstruction
• estimate pose between cross-modality image pairs
• register images from different sensors
• feature matching robust to style and modality changes

When to choose

• you need image matching that generalizes across modalities like sketches, paintings, or different sensors
• you want pre-trained weights for feature matching without training your own model
• you are doing SfM, SLAM, or image registration where standard matchers fail on modality gaps

When to avoid

• you need training code - it is not yet released
• you need a lightweight classical matcher like SIFT/ORB for same-modality images
• you need a permissively licensed production dependency - no license is specified

Facets

library · maturity active

computer-vision image-processing machine-learning deep-learning computer-vision image-processing machine-learning deep-learning python cross-platform image-matching feature-matching cross-modality pre-trained-models research-code 3d-reconstruction pose-estimation gpu

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markdown · JSON · MCP: product_card(name="zju3dv/MatchAnything")