Feature Matcher Types

The following feature matcher types are available:

• SIFT_BRUTEFORCE: Brute-force matching optimized for SIFT descriptors (default). Uses L2 distance with ratio test.

• ALIKED_BRUTEFORCE: Brute-force matching for ALIKED descriptors. Uses cosine similarity. Requires ONNX support to be enabled at build time.

• SIFT_LIGHTGLUE: Neural network-based matching using the LightGlue model for SIFT descriptors. This typically produces more matches and higher inlier ratios than brute-force matching, especially for challenging image pairs with large viewpoint or illumination changes. Requires ONNX support to be enabled at build time.

• ALIKED_LIGHTGLUE: Neural network-based matching using the LightGlue model for ALIKED descriptors. Requires ONNX support to be enabled at build time.

To select a feature matcher type via the command-line:

$ colmap exhaustive_matcher \
    --database_path $DATASET_PATH/database.db \
    --FeatureMatching.type ALIKED_BRUTEFORCE \
    --AlikedMatching.min_cossim 0.85

For SIFT matching (the default):

$ colmap exhaustive_matcher \
    --database_path $DATASET_PATH/database.db \
    --FeatureMatching.type SIFT_BRUTEFORCE \
    --SiftMatching.max_ratio 0.8

In the GUI, open Processing > Feature matching, select any matching tab (Exhaustive, Sequential, etc.), and choose the matcher type from the “Type” dropdown in the shared options section.

Compatible Extractor and Matcher Types

The feature extractor and matcher types should be compatible:

• Use SIFT extraction with SIFT_BRUTEFORCE or SIFT_LIGHTGLUE matching.

• Use ALIKED_* extraction with ALIKED_BRUTEFORCE or ALIKED_LIGHTGLUE matching.

Mixing incompatible types (e.g., SIFT features with ALIKED matcher) will result in a runtime error. Do not mix different feature extractor types (e.g., SIFT and ALIKED) in the same database.

ALIKED Model Variants

ALIKED requires an ONNX model file. Several model variants are available with different trade-offs between speed and accuracy:

• aliked-n16rot: Faster and trained for some viewpoint invariance. 128-dim descriptors.

• aliked-n32: More expensive but not explicitly trained for viewpoint invariance, 128-dim descriptors.

Specify the model path using --AlikedExtraction.*_model_path. If the path is a URL, COLMAP will automatically download and cache the model. You can download different ALIKED models from the release page at https://github.com/colmap/colmap/releases/