# zju3dv/EfficientLoFTR

Code for "Efficient LoFTR: Semi-Dense Local Feature Matching with Sparse-Like Speed", CVPR 2024

Repository: https://github.com/zju3dv/EfficientLoFTR
Canonical: https://ross.abutalabs.com/products/efficientloftr
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Last push: 2025-07-30T13:19:42+00:00

## Health v2 (maintenance only)
Score: 40/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 34, release rhythm 35, longevity 64
- inputs: {"age_days": 909, "days_push": 399, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1042, forks 112 (observed 2026-08-28T04:03:20.755113+00:00)

## What it is
Efficient LoFTR is a PyTorch implementation of a semi-dense local feature matching model that matches keypoints between image pairs with sparse-like speed, published at CVPR 2024. It provides inference and training code, pretrained indoor/outdoor models, and is also available via Hugging Face Transformers.

## Use cases
- match keypoints between two images
- estimate camera pose from image pairs
- feature matching for 3D reconstruction
- image registration for UAV localization
- feature matching for autonomous driving
- run semi-dense matching in real time on GPU

## When to choose
- you need fast semi-dense feature matching between image pairs
- you want pretrained indoor/outdoor matching models with training code
- you need a matcher for SfM, SLAM, or visual localization pipelines

## When to avoid
- you need CPU-only real-time matching
- you need a simple classical detector like SIFT/ORB without deep learning dependencies

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, deep-learning, computer-vision, image-processing
- domain: computer-vision, machine-learning, deep-learning, robotics
- platform: python, cross-platform
- tags: feature-matching, loftr, cvpr-2024, pose-estimation, image-registration, 3d-reconstruction, pytorch, gpu, linux

## Member repositories
- zju3dv/EfficientLoFTR (main) score 40

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:20.755113+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:02:49.455586+00:00, confidence not recorded.
  - readme: https://github.com/zju3dv/EfficientLoFTR (fetched 2026-08-28T04:03:20.755113+00:00, sha 5f29a59c3138)
- Data as of 2026-08-30T08:39:29.467469+00:00.