---

name: lightglue description: "Use LightGlue for local feature matching, extractor selection, matcher configuration, visualization, and benchmarking." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LightGlue

Use this repo skill when a task involves LightGlue local feature matching: matching two images, choosing a supported feature extractor, validating feature dictionaries, configuring the matcher, plotting matches/pruning, or benchmarking latency.

LightGlue is an inference package for sparse local feature matching. It extracts or consumes keypoints/descriptors for two images and returns mutual correspondences plus scores and adaptive-depth/pruning diagnostics.

First checks

Install the public package or a local checkout with its runtime dependencies:

python -m pip install git+https://github.com/cvg/LightGlue.git
# or, in a local checkout:
python -m pip install -e .

Minimal import check:

from lightglue import LightGlue, SuperPoint, DISK, SIFT, ALIKED, DoGHardNet, match_pair
from lightglue.utils import load_image, rbd

Download-free API smoke check from this skill:

python scripts/lightglue_smoke.py --device cpu

This smoke check validates imports and synthetic tensor shapes with features=None; it does not validate pretrained matching quality.

Route by task

If the user asks to...	Read
Match two image files, save a match plot, use match_pair, or interpret coordinate correspondences.	sub-skills/image-pair-matching/SKILL.md
Choose/configure SuperPoint, DISK, ALIKED, SIFT, or DoGHardNet, or validate precomputed feature dictionaries.	sub-skills/extractors-and-features/SKILL.md
Call LightGlue directly, set thresholds, use features=None, disable adaptivity, compile, or interpret output keys.	sub-skills/matcher-configuration/SKILL.md
Benchmark latency/throughput, use FlashAttention or torch.compile, tune pruning thresholds, or plot matches/keypoints/pruning.	sub-skills/performance-and-visualization/SKILL.md

Shared references and scripts

• references/package-overview.md: package purpose, dependencies, public entry points, feature families, first-use weights, and backend stance.
• references/troubleshooting.md: cross-cutting install/import, network/cache, backend, and data-shape failures.
• references/repo-provenance.md: source commit, package version, evidence paths, and refresh baseline.
• references/repo-routing-metadata.json: structured scenario metadata for repo-skill routing.
• scripts/lightglue_smoke.py: deterministic import and synthetic matcher smoke check with no pretrained downloads.

Operating defaults

• Use SIFT for offline feature-schema checks because the default OpenCV SIFT extractor does not download neural weights.
• Use feature-specific matcher presets (features="superpoint", "disk", "aliked", "sift", or "doghardnet") for real pretrained matching; they may download matcher weights on first use.
• Use features=None only for precomputed/custom descriptors or API smoke checks. Random/untrained weights are not a matching-quality proof.
• CPU is adequate for import checks, schema validation, and tiny smoke tests. CUDA/MPS/FlashAttention are optional acceleration paths, not required package semantics.
• Do not send users to original repo files for runtime instructions; use the bundled references and scripts in this skill tree.

Non-goals

This skill does not cover training LightGlue from scratch, full structure-from-motion/localization pipelines, third-party ONNX/TensorRT export projects, or generic computer-vision tasks unrelated to local feature matching.