# JiawangBian/GMS-Feature-Matcher

GMS: Grid-based Motion Statistics for Fast, Ultra-robust Feature Correspondence (CVPR 17 & IJCV 20)

Repository: https://github.com/JiawangBian/GMS-Feature-Matcher
Canonical: https://ross.abutalabs.com/products/gms-feature-matcher
Homepage: http://jwbian.net/gms
Language: Python
License: BSD-3-Clause
License Family: permissive
Topics: gms, sfm, feature, matching, slam
Last push: 2020-06-10T06:30:44+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3598, "days_push": 2275, "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 1114, forks 358 (observed 2026-08-28T04:03:38.197229+00:00)

## What it is
GMS (Grid-based Motion Statistics) is a fast, robust feature correspondence algorithm for filtering feature matches between images, published at CVPR 2017 and IJCV 2020. It provides C++, Python, and Matlab implementations and has been integrated into OpenCV's xfeatures2d module.

## Use cases
- filter out bad feature matches between two images
- robust feature correspondence for SLAM or visual odometry
- match ORB or SIFT features quickly
- find image correspondences for structure from motion
- remove outliers from nearest-neighbor feature matches
- integrate a fast match filter into an OpenCV pipeline

## When to choose
- you need fast, robust filtering of feature matches in real-time vision systems like SLAM
- you want a lightweight alternative to RANSAC for match outlier rejection
- you are already using OpenCV and can leverage the built-in matchGMS

## When to avoid
- you need deep-learning-based matchers for extreme viewpoint or appearance changes
- you need a maintained project with recent updates - the repo has not been updated since 2020
- you need matching across image collections rather than image pairs

## Facets
- artifact type: library
- maturity: maintenance
- function: computer-vision, image-processing
- domain: computer-vision, image-processing, robotics
- platform: cpp, python, cross-platform
- tags: feature-matching, gms, slam, sfm, orb, sift, opencv, cvpr

## Member repositories
- JiawangBian/GMS-Feature-Matcher (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:38.197229+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 2026-08-30T06:42:12.656420+00:00, confidence not recorded.
  - readme: https://github.com/JiawangBian/GMS-Feature-Matcher (fetched 2026-08-28T04:03:38.197229+00:00, sha e8628a7d1f12)
- Data as of 2026-08-30T08:39:29.467469+00:00.