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<!DOCTYPE html>
<htmllang="en" data-content_root="./" data-theme="auto">
<head>
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<metaproperty="og:title" content="Command-line Interface" />
<metaproperty="og:type" content="website" />
<metaproperty="og:url" content="https://colmap.github.io/cli.html" />
<metaproperty="og:site_name" content="COLMAP" />
<metaproperty="og:description" content="The command-line interface provides access to all of COLMAP’s functionality for automated scripting. Each core functionality is implemented as a command to the colmap executable. Run colmap -h to l..." />
<metaproperty="og:image" content="https://colmap.github.io/_static/og-image.png" />
<metaproperty="og:image:alt" content="COLMAP — Structure-from-Motion & Multi-View Stereo" />
<metaname="description" content="The command-line interface provides access to all of COLMAP’s functionality for automated scripting. Each core functionality is implemented as a command to the colmap executable. Run colmap -h to l..." />
<metaname="twitter:card" content="summary_large_image" />
<title>Command-line Interface — COLMAP</title>
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<linkrel="prev" title="Graphical User Interface" href="gui.html" />
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autocomplete="off"
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<spanclass="search-button__kbd-shortcut"><kbdclass="kbd-shortcut__modifier">Ctrl</kbd>+<kbd>K</kbd></span>
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Installation
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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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Command-line Interface
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Tutorial
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Key Concepts
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3D Viewer
</a>
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Feature Extraction and Matching
</a>
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<aclass="nav-link nav-internal" href="database.html">
Database Format
</a>
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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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Command-line Interface
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PyCOLMAP
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Frequently Asked Questions
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Changelog
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<sectionid="command-line-interface">
<spanid="cli"></span><h1>Command-line Interface<aclass="headerlink" href="#command-line-interface" title="Link to this heading">#</a></h1>
<p>The command-line interface provides access to all of COLMAP’s functionality for
automated scripting. Each core functionality is implemented as a command to the
<codeclass="docutils literal notranslate"><spanclass="pre">colmap</span></code> executable. Run <codeclass="docutils literal notranslate"><spanclass="pre">colmap</span><spanclass="pre">-h</span></code> to list the available commands (or
<codeclass="docutils literal notranslate"><spanclass="pre">COLMAP.bat</span><spanclass="pre">-h</span></code> under Windows). Note that if you run COLMAP from the CMake
build folder, the executable is located at <codeclass="docutils literal notranslate"><spanclass="pre">./src/colmap/exe/colmap</span></code>. To start the
graphical user interface, run <codeclass="docutils literal notranslate"><spanclass="pre">colmap</span><spanclass="pre">gui</span></code>.</p>
<sectionid="example">
<h2>Example<aclass="headerlink" href="#example" title="Link to this heading">#</a></h2>
<p>Assuming you stored the images of your project in the following structure:</p>
<divclass="highlight-default notranslate"><divclass="highlight"><pre><span></span>/path/to/project/...
+── images
│ +── image1.jpg
│ +── image2.jpg
│ +── ...
│ +── imageN.jpg
</pre></div>
</div>
<p>The command for the automatic reconstruction tool would be:</p>
<divclass="highlight-default notranslate"><divclass="highlight"><pre><span></span># The project folder must contain a folder "images" with all the images.
$ DATASET_PATH=/path/to/project
$ colmap automatic_reconstructor \
--workspace_path $DATASET_PATH \
--image_path $DATASET_PATH/images
</pre></div>
</div>
<p>Note that any command lists all available options using the <codeclass="docutils literal notranslate"><spanclass="pre">-h,--help</span></code>
command-line argument. In case you need more control over the individual
parameters of the reconstruction process, you can execute the following sequence
of commands as an alternative to the automatic reconstruction command:</p>
<divclass="highlight-default notranslate"><divclass="highlight"><pre><span></span># The project folder must contain a folder "images" with all the images.
$ DATASET_PATH=/path/to/dataset
$ colmap feature_extractor \
--database_path $DATASET_PATH/database.db \
--image_path $DATASET_PATH/images
$ colmap exhaustive_matcher \
--database_path $DATASET_PATH/database.db
$ mkdir -p $DATASET_PATH/sparse
$ colmap mapper \
--database_path $DATASET_PATH/database.db \
--image_path $DATASET_PATH/images \
--output_path $DATASET_PATH/sparse
$ mkdir -p $DATASET_PATH/dense
$ colmap image_undistorter \
--image_path $DATASET_PATH/images \
--input_path $DATASET_PATH/sparse/0 \
--output_path $DATASET_PATH/dense \
--output_type COLMAP \
--max_image_size 2000
$ colmap patch_match_stereo \
--workspace_path $DATASET_PATH/dense \
--workspace_format COLMAP \
--PatchMatchStereo.geom_consistency true
$ colmap stereo_fusion \
--workspace_path $DATASET_PATH/dense \
--workspace_format COLMAP \
--input_type geometric \
--output_path $DATASET_PATH/dense/fused.ply
$ colmap poisson_mesher \
--input_path $DATASET_PATH/dense/fused.ply \
--output_path $DATASET_PATH/dense/meshed-poisson.ply
$ colmap delaunay_mesher \
--input_path $DATASET_PATH/dense \
--output_path $DATASET_PATH/dense/meshed-delaunay.ply
$ colmap advancing_front_mesher \
--input_path $DATASET_PATH/dense \
--output_path $DATASET_PATH/dense/meshed-advancing-front.ply
# Optionally simplify a dense mesh to reduce its size.
$ colmap mesh_simplifier \
--input_path $DATASET_PATH/dense/meshed-poisson.ply \
--output_path $DATASET_PATH/dense/meshed-poisson-simplified.ply \
--MeshSimplification.target_face_ratio 0.25
# Optionally texture a mesh using the undistorted images.
$ colmap mesh_texturer \
--workspace_path $DATASET_PATH/dense \
--input_path $DATASET_PATH/dense/meshed-poisson.ply \
--output_path $DATASET_PATH/dense/textured
</pre></div>
</div>
<p>To use the global SfM pipeline instead of the incremental mapper, replace the
<codeclass="docutils literal notranslate"><spanclass="pre">mapper</span></code> step with <codeclass="docutils literal notranslate"><spanclass="pre">global_mapper</span></code>. The global mapper depends on good focal
length priors, so if reliable intrinsics are not available (e.g., from EXIF or
lab calibration), you should run <codeclass="docutils literal notranslate"><spanclass="pre">view_graph_calibrator</span></code> first. This step is
optional but recommended to improve the quality of global SfM, as was always
the default in <aclass="reference external" href="https://github.com/colmap/glomap">GLOMAP</a>. Note that
<codeclass="docutils literal notranslate"><spanclass="pre">view_graph_calibrator</span></code> modifies camera intrinsics and two-view geometries
in the database in-place, so it is recommended to work on a copy of the
database:</p>
<divclass="highlight-default notranslate"><divclass="highlight"><pre><span></span>$ colmap feature_extractor \
--database_path $DATASET_PATH/database.db \
--image_path $DATASET_PATH/images
$ colmap exhaustive_matcher \
--database_path $DATASET_PATH/database.db
# Optional but often needed: calibrate intrinsics from the view graph.
# This modifies the database in-place, so work on a copy.
$ cp $DATASET_PATH/database.db $DATASET_PATH/database_global.db
$ colmap view_graph_calibrator \
--database_path $DATASET_PATH/database_global.db
$ mkdir -p $DATASET_PATH/sparse
$ colmap global_mapper \
--database_path $DATASET_PATH/database_global.db \
--image_path $DATASET_PATH/images \
--output_path $DATASET_PATH/sparse
</pre></div>
</div>
<p>If you want to run COLMAP on a computer without an attached display (e.g.,
cluster or cloud service), COLMAP automatically switches to use CUDA if
supported by your system. If no CUDA enabled device is available, you can
manually select to use CPU-based feature extraction and matching by setting the
<codeclass="docutils literal notranslate"><spanclass="pre">--FeatureExtraction.use_gpu</span><spanclass="pre">0</span></code> and <codeclass="docutils literal notranslate"><spanclass="pre">--FeatureMatching.use_gpu</span><spanclass="pre">0</span></code> options.</p>
</section>
<sectionid="help">
<h2>Help<aclass="headerlink" href="#help" title="Link to this heading">#</a></h2>
<p>The available commands can be listed using the command:</p>
<divclass="highlight-default notranslate"><divclass="highlight"><pre><span></span>$ colmap help
Usage:
colmap [command] [options]
Documentation:
https://colmap.github.io/
Example usage:
colmap help [ -h, --help ]
colmap gui
colmap gui -h [ --help ]
colmap automatic_reconstructor -h [ --help ]
colmap automatic_reconstructor --image_path IMAGES --workspace_path WORKSPACE
colmap feature_extractor --image_path IMAGES --database_path DATABASE
colmap exhaustive_matcher --database_path DATABASE
colmap mapper --image_path IMAGES --database_path DATABASE --output_path MODEL
...
Available commands:
help
gui
automatic_reconstructor
bundle_adjuster
color_extractor
database_cleaner
database_creator
database_merger
delaunay_mesher
exhaustive_matcher
feature_extractor
feature_importer
geometric_verifier
global_mapper
guided_geometric_verifier
hierarchical_mapper
image_deleter
image_filterer
image_rectifier
image_registrator
image_undistorter
image_undistorter_standalone
mapper
matches_importer
mesh_simplifier
mesh_texturer
model_aligner
model_analyzer
model_clusterer
model_comparer
model_converter
model_cropper
model_merger
model_orientation_aligner
model_splitter
model_transformer
patch_match_stereo
point_filtering
point_triangulator
pose_prior_mapper
poisson_mesher
project_generator
rig_configurator
rotation_averager
sequential_matcher
spatial_matcher
stereo_fusion
transitive_matcher
view_graph_calibrator
vocab_tree_builder
vocab_tree_matcher
vocab_tree_retriever
</pre></div>
</div>
<p>And each command has a <codeclass="docutils literal notranslate"><spanclass="pre">-h,--help</span></code> command-line argument to show the usage and
the available options, e.g.:</p>
<divclass="highlight-default notranslate"><divclass="highlight"><pre><span></span>$ colmap feature_extractor -h
Options can either be specified via command-line or by defining
them in a .ini project file passed to ``--project_path``.
-h [ --help ]
--default_random_seed arg (=0)
--log_target arg (=stderr_and_file)
{stderr, stdout, file, stderr_and_file}
--log_path arg
--log_level arg (=0)
--log_severity arg (=0) 0:INFO, 1:WARNING, 2:ERROR, 3:FATAL
--log_color arg (=1)
--project_path arg
--database_path arg
--image_path arg
--camera_mode arg (=-1)
--image_list_path arg
--descriptor_normalization arg (=l1_root)
{'l1_root', 'l2'}
--ImageReader.mask_path arg
--ImageReader.camera_model arg (=SIMPLE_RADIAL)
--ImageReader.single_camera arg (=0)
--ImageReader.single_camera_per_folder arg (=0)
--ImageReader.single_camera_per_image arg (=0)
--ImageReader.existing_camera_id arg (=-1)
--ImageReader.camera_params arg
--ImageReader.default_focal_length_factor arg (=1.2)
--ImageReader.camera_mask_path arg
--FeatureExtraction.type arg (=SIFT)
--FeatureExtraction.max_image_size arg (=3200)
--FeatureExtraction.num_threads arg (=-1)
--FeatureExtraction.use_gpu arg (=1)
--FeatureExtraction.gpu_index arg (=-1)
--SiftExtraction.max_num_features arg (=8192)
--SiftExtraction.first_octave arg (=-1)
--SiftExtraction.num_octaves arg (=4)
--SiftExtraction.octave_resolution arg (=3)
--SiftExtraction.peak_threshold arg (=0.0066666666666666671)
--SiftExtraction.edge_threshold arg (=10)
--SiftExtraction.estimate_affine_shape arg (=0)
--SiftExtraction.max_num_orientations arg (=2)
--SiftExtraction.upright arg (=0)
--SiftExtraction.domain_size_pooling arg (=0)
--SiftExtraction.dsp_min_scale arg (=0.16666666666666666)
--SiftExtraction.dsp_max_scale arg (=3)
--SiftExtraction.dsp_num_scales arg (=10)
</pre></div>
</div>
<p>The available options can either be provided directly from the command-line or
through a <codeclass="docutils literal notranslate"><spanclass="pre">.ini</span></code> file provided to <codeclass="docutils literal notranslate"><spanclass="pre">--project_path</span></code>.</p>
</section>
<sectionid="commands">
<h2>Commands<aclass="headerlink" href="#commands" title="Link to this heading">#</a></h2>
<p>The following list briefly documents the functionality of each command, that is
available as <codeclass="docutils literal notranslate"><spanclass="pre">colmap</span><spanclass="pre">[command]</span></code>:</p>
<ulclass="simple">
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">gui</span></code>: The graphical user interface, see
<aclass="reference internal" href="gui.html#gui"><spanclass="std std-ref">Graphical User Interface</span></a> for more information.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">automatic_reconstructor</span></code>: Automatically reconstruct sparse and dense model
for a set of input images. Key options include <codeclass="docutils literal notranslate"><spanclass="pre">--quality</span></code> (LOW, MEDIUM,
HIGH, EXTREME), <codeclass="docutils literal notranslate"><spanclass="pre">--data_type</span></code> (INDIVIDUAL, VIDEO, INTERNET) to tune settings
for different capture scenarios, <codeclass="docutils literal notranslate"><spanclass="pre">--feature</span></code> (SIFT, ALIKED, LOMA, LOMA128)
to select the feature extraction algorithm, <codeclass="docutils literal notranslate"><spanclass="pre">--mapper</span></code> (INCREMENTAL,
HIERARCHICAL, GLOBAL) to choose the SfM pipeline, and <codeclass="docutils literal notranslate"><spanclass="pre">--mesher</span></code> (POISSON,
DELAUNAY, ADVANCING_FRONT) to select the surface reconstruction method.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">project_generator</span></code>: Generate project files at different quality settings.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">feature_extractor</span></code>, <codeclass="docutils literal notranslate"><spanclass="pre">feature_importer</span></code>: Perform feature extraction or
import features for a set of images.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">exhaustive_matcher</span></code>, <codeclass="docutils literal notranslate"><spanclass="pre">vocab_tree_matcher</span></code>, <codeclass="docutils literal notranslate"><spanclass="pre">sequential_matcher</span></code>,
<codeclass="docutils literal notranslate"><spanclass="pre">spatial_matcher</span></code>, <codeclass="docutils literal notranslate"><spanclass="pre">transitive_matcher</span></code>, <codeclass="docutils literal notranslate"><spanclass="pre">matches_importer</span></code>:
Perform feature matching after performing feature extraction.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">geometric_verifier</span></code>: Run standalone geometric verification on existing
feature matches in the database. This estimates two-view geometries
(fundamental/essential matrices, homographies) for matched image pairs.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">guided_geometric_verifier</span></code>: Run geometric verification guided by an
existing sparse reconstruction. Uses the known relative camera poses to
improve match verification results.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">mapper</span></code>: Sparse 3D reconstruction / mapping of the dataset using SfM after
performing feature extraction and matching.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">global_mapper</span></code>: Sparse 3D reconstruction using the global SfM pipeline.
Unlike the incremental <codeclass="docutils literal notranslate"><spanclass="pre">mapper</span></code>, the global approach solves for all camera
poses simultaneously using rotation averaging and global positioning. This
can be faster for large datasets but may be less robust to outliers.
The global mapper depends on reasonably good focal length priors to perform
well. Run <codeclass="docutils literal notranslate"><spanclass="pre">view_graph_calibrator</span></code> before <codeclass="docutils literal notranslate"><spanclass="pre">global_mapper</span></code> to calibrate
camera intrinsics and estimate relative poses from the view graph, or provide
camera calibrations manually.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">pose_prior_mapper</span></code>: Sparse 3D reconstruction / mapping using pose priors.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">hierarchical_mapper</span></code>: Sparse 3D reconstruction / mapping of the dataset
using hierarchical SfM after performing feature extraction and matching.
This parallelizes the reconstruction process by partitioning the scene into
overlapping submodels and then reconstructing each submodel independently.
Finally, the overlapping submodels are merged into a single reconstruction.
It is recommended to run a few rounds of point triangulation and bundle
adjustment after this step.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">image_undistorter</span></code>: Undistort images and/or export them for MVS or to
external dense reconstruction software, such as CMVS/PMVS.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">image_rectifier</span></code>: Stereo rectify cameras and undistort images for stereo
disparity estimation.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">image_filterer</span></code>: Filter images from a sparse reconstruction.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">image_deleter</span></code>: Delete specific images from a sparse reconstruction.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">patch_match_stereo</span></code>: Dense 3D reconstruction / mapping using MVS after
running the <codeclass="docutils literal notranslate"><spanclass="pre">image_undistorter</span></code> to initialize the workspace.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">stereo_fusion</span></code>: Fusion of <codeclass="docutils literal notranslate"><spanclass="pre">patch_match_stereo</span></code> results into to a colored
point cloud.</p></li>
<li><p><codeclass="docutils literal notranslate"><spanclass="pre">poisson_mesher</span></code>: Meshing of the fused point cloud using Poisson