Command-line Interface

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 list the available commands (or COLMAP.bat -h under Windows). Note that if you run COLMAP from the CMake build folder, the executable is located at ./src/colmap/exe/colmap. To start the graphical user interface, run colmap gui.

Example

Assuming you stored the images of your project in the following structure:

/path/to/project/...
+── images
│   +── image1.jpg
│   +── image2.jpg
│   +── ...
│   +── imageN.jpg

The command for the automatic reconstruction tool would be:

# 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

Note that any command lists all available options using the -h,--help 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:

# 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

# 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

To use the global SfM pipeline instead of the incremental mapper, replace the mapper step with global_mapper. 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 view_graph_calibrator first. This step is optional but recommended to improve the quality of global SfM, as was always the default in GLOMAP. Note that view_graph_calibrator modifies camera intrinsics and two-view geometries in the database in-place, so it is recommended to work on a copy of the database:

$ 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

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 --FeatureExtraction.use_gpu 0 and --FeatureMatching.use_gpu 0 options.

Help

The available commands can be listed using the command:

$ 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

And each command has a -h,--help command-line argument to show the usage and the available options, e.g.:

$ 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)

The available options can either be provided directly from the command-line or through a .ini file provided to --project_path.

Commands

The following list briefly documents the functionality of each command, that is available as colmap [command]:

• gui: The graphical user interface, see Graphical User Interface for more information.

• automatic_reconstructor: Automatically reconstruct sparse and dense model for a set of input images. Key options include --quality (LOW, MEDIUM, HIGH, EXTREME), --data_type (INDIVIDUAL, VIDEO, INTERNET) to tune settings for different capture scenarios, --feature (SIFT, ALIKED) to select the feature extraction algorithm, --mapper (INCREMENTAL, HIERARCHICAL, GLOBAL) to choose the SfM pipeline, and --mesher (POISSON, DELAUNAY) to select the surface reconstruction method.

• project_generator: Generate project files at different quality settings.

• feature_extractor, feature_importer: Perform feature extraction or import features for a set of images.

• exhaustive_matcher, vocab_tree_matcher, sequential_matcher, spatial_matcher, transitive_matcher, matches_importer: Perform feature matching after performing feature extraction.

• geometric_verifier: Run standalone geometric verification on existing feature matches in the database. This estimates two-view geometries (fundamental/essential matrices, homographies) for matched image pairs.

• guided_geometric_verifier: Run geometric verification guided by an existing sparse reconstruction. Uses the known relative camera poses to improve match verification results.

• mapper: Sparse 3D reconstruction / mapping of the dataset using SfM after performing feature extraction and matching.

• global_mapper: Sparse 3D reconstruction using the global SfM pipeline. Unlike the incremental mapper, 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 view_graph_calibrator before global_mapper to calibrate camera intrinsics and estimate relative poses from the view graph, or provide camera calibrations manually.

• pose_prior_mapper: Sparse 3D reconstruction / mapping using pose priors.

• hierarchical_mapper: 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.

• image_undistorter: Undistort images and/or export them for MVS or to external dense reconstruction software, such as CMVS/PMVS.

• image_rectifier: Stereo rectify cameras and undistort images for stereo disparity estimation.

• image_filterer: Filter images from a sparse reconstruction.

• image_deleter: Delete specific images from a sparse reconstruction.

• patch_match_stereo: Dense 3D reconstruction / mapping using MVS after running the image_undistorter to initialize the workspace.

• stereo_fusion: Fusion of patch_match_stereo results into to a colored point cloud.

• poisson_mesher: Meshing of the fused point cloud using Poisson surface reconstruction.

• delaunay_mesher: Meshing of the reconstructed sparse or dense point cloud using a graph cut on the Delaunay triangulation and visibility voting.

• mesh_simplifier: Simplify a triangle mesh (PLY format) using Quadric Error Metric (QEM) decimation. This reduces the number of faces in a mesh while preserving its overall shape and appearance. Key options include --MeshSimplification.target_face_ratio to control the fraction of faces to retain (default 0.1), --MeshSimplification.max_error to set a maximum quadric error threshold (0 = disabled), and --MeshSimplification.boundary_weight to control boundary edge preservation (default 1000). Supports multi-threaded initialization via --MeshSimplification.num_threads.

• mesh_texturer: Produce a texture atlas and UV coordinates for a triangle mesh using calibrated multi-view images.

• image_registrator: Register new images in the database against an existing model, e.g., when extracting features and matching newly added images in a database after running mapper. Note that no bundle adjustment or triangulation is performed.

• point_triangulator: Triangulate all observations of registered images in an existing model using the feature matches in a database.

• point_filtering: Filter sparse points in model by enforcing criteria, such as minimum track length, maximum reprojection error, etc.

• bundle_adjuster: Run global bundle adjustment on a reconstructed scene, e.g., when a refinement of the intrinsics is needed or after running the image_registrator.

• database_cleaner: Clean specific or all database tables.

• database_creator: Create an empty COLMAP SQLite database with the necessary database schema information.

• database_merger: Merge two databases into a new database. Note that the cameras will not be merged and that the unique camera and image identifiers might change during the merging process.

• model_analyzer: Print statistics about reconstructions.

• model_clusterer: Split a reconstruction into smaller sub-model clusters. Useful for managing and processing large-scale reconstructions.

• model_aligner: Align/geo-register model to coordinate system of given camera centers.

• model_orientation_aligner: Align the coordinate axis of a model using a Manhattan world assumption.

• model_comparer: Compare statistics of two reconstructions.

• model_converter: Convert the COLMAP export format to another format, such as PLY or NVM.

• model_cropper: Crop model to specific bounding box described in GPS or model coordinate system.

• model_merger: Attempt to merge two disconnected reconstructions, if they have common registered images.

• model_splitter: Divide model in rectangular sub-models specified from file containing bounding box coordinates, or max extent of sub-model, or number of subdivisions in each dimension.

• model_transformer: Transform coordinate frame of a model.

• color_extractor: Extract mean colors for all 3D points of a model.

• rig_configurator: Configure rigs and frames after feature extraction.

• vocab_tree_builder: Create a vocabulary tree from a database with extracted images. This is an offline procedure and can be run once, while the same vocabulary tree can be reused for other datasets. Note that, as a rule of thumb, you should use at least 10-100 times more features than visual words. Pre-trained trees can be downloaded from https://demuc.de/colmap/. This is useful if you want to build a custom tree with a different trade-off in terms of precision/recall vs. speed.

• vocab_tree_retriever: Perform vocabulary tree based image retrieval.

• rotation_averager: Run standalone rotation averaging on the view graph. Estimates global camera rotations from pairwise relative rotations.

• view_graph_calibrator: Calibrate camera intrinsics using the view graph. Estimates focal lengths and other intrinsic parameters from pairwise geometric relations. Should be run before global_mapper, if no good prior camera intrinsics are known, since the global mapper depends on reasonably good focal length priors to perform well.

Visualization

If you want to quickly visualize the outputs of the sparse or dense reconstruction pipelines, COLMAP offers you the following possibilities:

• The sparse point cloud obtained with the mapper can be visualized via the COLMAP GUI by importing the model files: choose File > Import Model and select the folder containing the sparse model files (cameras.txt, images.txt, points3D.txt, etc.).

• The dense point cloud obtained with the stereo_fusion can be visualized via the COLMAP GUI by importing fused.ply: choose File > Import Model from... and then select the file fused.ply.

• The dense mesh model meshed-*.ply obtained with the poisson_mesher or the delaunay_mesher can currently not be visualized with COLMAP, instead you can use an external viewer, such as Meshlab. Use the mesh_simplifier command to reduce the mesh size for faster visualization or downstream processing. Use the mesh_texturer command to produce a textured mesh with a texture atlas that can be visualized in Meshlab or other 3D viewers.