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<!DOCTYPE html>
<html lang="en" data-content_root="./" data-theme="auto">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="viewport" content="width=device-width, initial-scale=1" />
<meta property="og:title" content="Feature Extraction and Matching" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://colmap.github.io/features.html" />
<meta property="og:site_name" content="COLMAP" />
<meta property="og:description" content="COLMAP supports multiple feature extraction and matching algorithms. This page describes how to switch between them using the command-line interface or the graphical user interface. Feature Extract..." />
<meta property="og:image" content="https://colmap.github.io/_static/og-image.png" />
<meta property="og:image:alt" content="COLMAP — Structure-from-Motion & Multi-View Stereo" />
<meta name="description" content="COLMAP supports multiple feature extraction and matching algorithms. This page describes how to switch between them using the command-line interface or the graphical user interface. Feature Extract..." />
<meta name="twitter:card" content="summary_large_image" />
<title>Feature Extraction and Matching — COLMAP</title>
<script data-cfasync="false">
document.documentElement.dataset.mode = localStorage.getItem("mode") || "auto";
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<link rel="canonical" href="https://colmap.github.io/features.html" />
<link rel="icon" href="_static/favicon.svg"/>
<link rel="index" title="Index" href="genindex.html" />
<link rel="search" title="Search" href="search.html" />
<link rel="next" title="Database Format" href="database.html" />
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<meta name="viewport" content="width=device-width, initial-scale=1"/>
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<meta name="docsearch:language" content="en"/>
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action="search.html"
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class="form-control"
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placeholder="Search the docs ..."
aria-label="Search the docs ..."
autocomplete="off"
autocorrect="off"
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Tutorial
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Key Concepts
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3D Viewer
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Feature Extraction and Matching
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Database Format
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Camera Models
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Output Format
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Datasets
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Frequently Asked Questions
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Tutorial
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Key Concepts
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3D Viewer
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Feature Extraction and Matching
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<a class="nav-link nav-internal" href="database.html">
Database Format
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Camera Models
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Rig Support
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Output Format
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Datasets
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Graphical User Interface
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PyCOLMAP
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<section id="feature-extraction-and-matching">
<span id="features"></span><h1>Feature Extraction and Matching<a class="headerlink" href="#feature-extraction-and-matching" title="Link to this heading">#</a></h1>
<p>COLMAP supports multiple feature extraction and matching algorithms. This page
describes how to switch between them using the command-line interface or the
graphical user interface.</p>
<section id="feature-extractor-types">
<h2>Feature Extractor Types<a class="headerlink" href="#feature-extractor-types" title="Link to this heading">#</a></h2>
<p>The following feature extractor types are available:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">SIFT</span></code>: Scale-Invariant Feature Transform (default). The classic and most
widely tested feature extractor. Produces 128-dimensional uint8 descriptors.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ALIKED</span></code>: A Lighter Keypoint and Descriptor Extractor. A learned feature
extractor that produces floating-point descriptors. Requires ONNX support to
be enabled at build time (<code class="docutils literal notranslate"><span class="pre">-DONNX_ENABLED=ON</span></code>).</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA</span></code>: A learned feature extractor, introduced in LoMa: Local Feature
Matching Revisited, ECCV26, that uses the DeDoDe architecture. Two descriptor
variants are available: <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> (256-dim, frozen DINOv2 features combined
with trained convolutional features) and <code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code> (128-dim, more
lightweight). Requires ONNX support to be enabled at build time
(<code class="docutils literal notranslate"><span class="pre">-DONNX_ENABLED=ON</span></code>).</p></li>
</ul>
<p>To select a feature extractor type via the command-line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap feature_extractor \
--database_path $DATASET_PATH/database.db \
--image_path $DATASET_PATH/images \
--FeatureExtraction.type ALIKED_N16ROT \
--AlikedExtraction.max_num_features 2048
</pre></div>
</div>
<p>For SIFT (the default), you can omit the type or explicitly set it:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap feature_extractor \
--database_path $DATASET_PATH/database.db \
--image_path $DATASET_PATH/images \
--FeatureExtraction.type SIFT \
--SiftExtraction.max_num_features 8192
</pre></div>
</div>
<p>In the GUI, open <code class="docutils literal notranslate"><span class="pre">Processing</span> <span class="pre">></span> <span class="pre">Feature</span> <span class="pre">extraction</span></code> and select the desired
tab (SIFT, ALIKED, LoMa, etc.) before clicking Extract.</p>
</section>
<section id="feature-matcher-types">
<h2>Feature Matcher Types<a class="headerlink" href="#feature-matcher-types" title="Link to this heading">#</a></h2>
<p>The following feature matcher types are available:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">SIFT_BRUTEFORCE</span></code>: Brute-force matching optimized for SIFT descriptors
(default). Uses L2 distance with ratio test.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ALIKED_BRUTEFORCE</span></code>: Brute-force matching for ALIKED descriptors. Uses
cosine similarity. Requires ONNX support to be enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">SIFT_LIGHTGLUE</span></code>: 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.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">ALIKED_LIGHTGLUE</span></code>: Neural network-based matching using the LightGlue model
for ALIKED descriptors. Requires ONNX support to be enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_BRUTEFORCE</span></code>: Brute-force matching for either of the LoMa descriptors.
Uses cosine similarity. Requires ONNX support to be enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code>: Dedicated neural network matcher for the 256-dim <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code>
descriptor, comparable in size to LightGlue. Requires ONNX support to be
enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_R</span></code>: Same size as <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code>, but trained with rotation augmentation
for better robustness to rotated image pairs. Also matches the 256-dim
<code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> descriptor. Requires ONNX support to be enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_L</span></code>, <code class="docutils literal notranslate"><span class="pre">LOMA_G</span></code>: Larger dedicated matchers for the 256-dim
<code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> descriptor, in increasing order of model size and matching
quality. Requires ONNX support to be enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code>: Dedicated matcher for the lightweight 128-dim <code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code>
descriptor. Requires ONNX support to be enabled at build time.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">FLEXGLUE</span></code>: Neural network matcher operating on dense image features
(FlexGlue). It works with the keypoints of any feature extractor and ignores
the descriptors, which are not required. Since FlexGlue computes dense
features from the image pixels, the images are read during matching from
<code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.image_path</span></code> (required). The matcher commands have no
global <code class="docutils literal notranslate"><span class="pre">--image_path</span></code>, so <code class="docutils literal notranslate"><span class="pre">FlexGlueMatching.image_path</span></code> must also be set
in project files. FlexGlue also predicts whether two images are covisible at
all, and <code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.min_covisibility</span></code> (default 0.5, 0 disables it)
rejects all matches of pairs with a lower covisibility score, which suppresses
look-alike pairs of repeated structures. Guided matching is not supported.
Requires ONNX support to be enabled at build time; a GPU is strongly
recommended.</p></li>
</ul>
<p>To select a feature matcher type via the command-line:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap exhaustive_matcher \
--database_path $DATASET_PATH/database.db \
--FeatureMatching.type ALIKED_BRUTEFORCE \
--AlikedMatching.brute_force_min_cossim 0.85
</pre></div>
</div>
<p>For SIFT matching (the default):</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap exhaustive_matcher \
--database_path $DATASET_PATH/database.db \
--FeatureMatching.type SIFT_BRUTEFORCE \
--SiftMatching.max_ratio 0.8
</pre></div>
</div>
<p>In the GUI, open <code class="docutils literal notranslate"><span class="pre">Processing</span> <span class="pre">></span> <span class="pre">Feature</span> <span class="pre">matching</span></code>, select any matching tab
(Exhaustive, Sequential, etc.), and choose the matcher type from the “Type”
dropdown in the shared options section.</p>
</section>
<section id="compatible-extractor-and-matcher-types">
<h2>Compatible Extractor and Matcher Types<a class="headerlink" href="#compatible-extractor-and-matcher-types" title="Link to this heading">#</a></h2>
<p>The feature extractor and matcher types should be compatible:</p>
<ul class="simple">
<li><p>Use <code class="docutils literal notranslate"><span class="pre">SIFT</span></code> extraction with <code class="docutils literal notranslate"><span class="pre">SIFT_BRUTEFORCE</span></code> or <code class="docutils literal notranslate"><span class="pre">SIFT_LIGHTGLUE</span></code> matching.</p></li>
<li><p>Use <code class="docutils literal notranslate"><span class="pre">ALIKED_*</span></code> extraction with <code class="docutils literal notranslate"><span class="pre">ALIKED_BRUTEFORCE</span></code> or <code class="docutils literal notranslate"><span class="pre">ALIKED_LIGHTGLUE</span></code> matching.</p></li>
<li><p>Use <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> extraction with <code class="docutils literal notranslate"><span class="pre">LOMA_BRUTEFORCE</span></code>, <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code>, <code class="docutils literal notranslate"><span class="pre">LOMA_R</span></code>,
<code class="docutils literal notranslate"><span class="pre">LOMA_L</span></code>, or <code class="docutils literal notranslate"><span class="pre">LOMA_G</span></code> matching.</p></li>
<li><p>Use <code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code> extraction with <code class="docutils literal notranslate"><span class="pre">LOMA_BRUTEFORCE</span></code> or <code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code>
matching.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">FLEXGLUE</span></code> matching works with any extraction type, as it only uses the
keypoint locations.</p></li>
</ul>
<p>Mixing incompatible types (e.g., SIFT features with ALIKED matcher, or
<code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code> features with the <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> matcher) will result in a runtime
error. Do not mix different feature extractor types (e.g., SIFT and ALIKED)
in the same database.</p>
</section>
<section id="aliked-model-variants">
<h2>ALIKED Model Variants<a class="headerlink" href="#aliked-model-variants" title="Link to this heading">#</a></h2>
<p>ALIKED requires an ONNX model file. Several model variants are available with
different trade-offs between speed and accuracy:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">aliked-n16rot</span></code>: Faster and trained for some viewpoint invariance. 128-dim descriptors.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">aliked-n32</span></code>: More expensive but not explicitly trained for viewpoint invariance, 128-dim descriptors.</p></li>
</ul>
<p>Specify the model path using <code class="docutils literal notranslate"><span class="pre">--AlikedExtraction.*_model_path</span></code>. 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 <a class="github reference external" href="https://github.com/colmap/colmap/releases/">colmap/colmap</a></p>
</section>
<section id="loma-model-variants">
<h2>LoMa Model Variants<a class="headerlink" href="#loma-model-variants" title="Link to this heading">#</a></h2>
<p>LoMa, similar to ALIKED, uses a detect+describe+match framework. The descriptor
comes in two variants:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code>: 256-dim descriptor combining frozen DINOv2 features with trained
convolutional features.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code>: A more lightweight 128-dim descriptor.</p></li>
</ul>
<p>Each descriptor variant has its own dedicated matcher(s) with
different trade-offs between speed and accuracy:</p>
<ul class="simple">
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> descriptors can be matched with <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> (smallest, same size as
LightGlue), <code class="docutils literal notranslate"><span class="pre">LOMA_R</span></code> (same size as <code class="docutils literal notranslate"><span class="pre">LOMA_B</span></code> but trained with rotation
augmentation), <code class="docutils literal notranslate"><span class="pre">LOMA_L</span></code>, or <code class="docutils literal notranslate"><span class="pre">LOMA_G</span></code> (progressively larger, slower, and
more accurate), as well as <code class="docutils literal notranslate"><span class="pre">LOMA_BRUTEFORCE</span></code>.</p></li>
<li><p><code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code> descriptors can only be matched with <code class="docutils literal notranslate"><span class="pre">LOMA_B128</span></code> or
<code class="docutils literal notranslate"><span class="pre">LOMA_BRUTEFORCE</span></code>. Fastest option but least accurate out of the LoMa
matchers.</p></li>
</ul>
<p>By default, all LoMa model weights are downloaded automatically and cached
locally the first time they are used, so no manual setup is required to get
started. To use a different model, specify its path using
<code class="docutils literal notranslate"><span class="pre">--LomaExtraction.*_model_path</span></code> for extraction and
<code class="docutils literal notranslate"><span class="pre">--LomaMatching.*_model_path</span></code> for matching. As with ALIKED, if the path is a
URL, COLMAP will automatically download and cache it. Extraction and
matching also support an opt-in <code class="docutils literal notranslate"><span class="pre">use_bf16</span></code> mode
(<code class="docutils literal notranslate"><span class="pre">--LomaExtraction.use_bf16</span></code> / <code class="docutils literal notranslate"><span class="pre">--LomaMatching.use_bf16</span></code>) for faster
inference on CUDA GPUs with compute capability 8.0 (Ampere) or newer. On other
devices, the fp32 models are used.</p>
</section>
<section id="flexglue">
<h2>FlexGlue<a class="headerlink" href="#flexglue" title="Link to this heading">#</a></h2>
<p>FlexGlue encodes each image into a dense stride-8 feature map with a
DINOv3-ConvNeXt backbone (at a fixed 512x512 resolution), refines the feature
maps of both images jointly with a transformer, and predicts the
correspondence of every keypoint in the other image. Keypoints are then matched
by mutual nearest neighbors between the keypoints and the predicted
correspondences. Example usage:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap exhaustive_matcher \
--database_path $DATASET_PATH/database.db \
--FeatureMatching.type FLEXGLUE \
--FlexGlueMatching.image_path $DATASET_PATH/images
</pre></div>
</div>
<p>With <code class="docutils literal notranslate"><span class="pre">min_covisibility</span> <span class="pre">></span> <span class="pre">0</span></code>, the default models also stop early on pairs that
are confidently not covisible: after layers 2, 4, 6, and 8 of the 9 transformer
layers, a pair is rejected without running the remaining layers if its
covisibility score is below <code class="docutils literal notranslate"><span class="pre">min_covisibility</span> <span class="pre">*</span> <span class="pre">(1</span> <span class="pre">-</span> <span class="pre">early_stop_decay)^(9</span> <span class="pre">-</span>
<span class="pre">layer)</span></code>. Lower values of <code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.early_stop_decay</span></code> stop earlier,
and 1 disables early stopping. On the 22569 exhaustive pairs of ETH3D DSLR, the
default models with the default <code class="docutils literal notranslate"><span class="pre">early_stop_decay</span> <span class="pre">0.5</span></code> and
<code class="docutils literal notranslate"><span class="pre">min_covisibility</span> <span class="pre">0.5</span></code> stop early on 35% of the pairs, and only 6 of the
10288 pairs that are covisible according to the ground truth are rejected early
although they would pass <code class="docutils literal notranslate"><span class="pre">min_covisibility</span></code> after the last layer.</p>
<p>The dense features of the most recently used images are cached in memory
(<code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.feature_cache_size</span></code>, ~4MB per image), on the GPU if the
models run on a CUDA GPU. On CUDA GPUs with compute capability 7.0 or newer, a
variant of the matcher whose transformer layers run in float16 is used by
default (<code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.use_fp16</span></code>), which is faster than the float32
matcher with practically identical matches; the float32 matcher is used on the CPU, on
older GPUs, or if the float16 variant fails to initialize. On CUDA GPUs,
<code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.num_workers_per_gpu</span></code> (default 4) matching threads share a
single matcher instance and GPU feature cache per GPU so CPU image decoding
overlaps with GPU inference, while <code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.num_inferences_per_gpu</span></code>
(default 2) bounds the number of concurrent model inferences on the GPU (lower
it, e.g. to 1, to reduce GPU memory usage while keeping parallel CPU image
decoding). On the CPU, a
single matching thread runs the models with <code class="docutils literal notranslate"><span class="pre">--FeatureMatching.num_threads</span></code>
threads, rather than one copy of the models per thread. The
<code class="docutils literal notranslate"><span class="pre">automatic_reconstructor</span></code> provides the <code class="docutils literal notranslate"><span class="pre">--feature</span> <span class="pre">flexglue</span></code> preset, which
extracts the same DaD keypoints as the LoMa presets and matches them with
FlexGlue. The models are downloaded automatically, and custom models can
be specified with <code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.descriptor_model_path</span></code>,
<code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.matcher_model_path</span></code>, and
<code class="docutils literal notranslate"><span class="pre">--FlexGlueMatching.matcher_model_path_fp16</span></code>. The models are exported with
<code class="docutils literal notranslate"><span class="pre">onnx/export.py</span></code> from <a class="github reference external" href="https://github.com/cvg/FlexGlue">cvg/FlexGlue</a>. Custom matchers must
be exported with early stopping, e.g., <code class="docutils literal notranslate"><span class="pre">--early_stop_layers</span> <span class="pre">2,4,6,8</span></code>, since
the matcher requires the <code class="docutils literal notranslate"><span class="pre">early_stop_decay</span></code> input.</p>
</section>
<section id="iterative-runs">
<h2>Iterative Runs<a class="headerlink" href="#iterative-runs" title="Link to this heading">#</a></h2>
<p>When re-running matching on an existing database, image pairs that already have
a two-view geometry are skipped, so changing <code class="docutils literal notranslate"><span class="pre">--FeatureMatching.type</span></code> on such
a database leaves previously matched pairs untouched. To force re-matching, first
clear the existing results with <code class="docutils literal notranslate"><span class="pre">database_cleaner</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>$ colmap database_cleaner \
--database_path $DATASET_PATH/database.db \
--type two_view_geometries \
--type matches
</pre></div>
</div>
<p>When re-extracting with a different <code class="docutils literal notranslate"><span class="pre">--FeatureExtraction.type</span></code>, use
<code class="docutils literal notranslate"><span class="pre">--type</span> <span class="pre">features</span></code> to additionally clear the extracted keypoints and descriptors.</p>
</section>
</section>
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<li class="toc-h2 nav-item toc-entry"><a class="reference internal nav-link" href="#feature-extractor-types">Feature Extractor Types</a></li>
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