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/exe/colmap. To start the
graphical user interface, run
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
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 $DATASET_PATH/sparse $ colmap mapper \ --database_path $DATASET_PATH/database.db \ --image_path $DATASET_PATH/images \ --output_path $DATASET_PATH/sparse $ mkdir $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
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
--SiftExtraction.use_gpu 0 and
--SiftMatching.use_gpu 0 options.
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_creator delaunay_mesher exhaustive_matcher feature_extractor feature_importer image_deleter image_rectifier image_registrator image_undistorter mapper matches_importer model_aligner model_analyzer model_converter model_merger model_orientation_aligner patch_match_stereo point_triangulator poisson_mesher rig_bundle_adjuster sequential_matcher spatial_matcher stereo_fusion transitive_matcher 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 ] --project_path arg --database_path arg --image_path arg --image_list_path arg --ImageReader.camera_model arg (=SIMPLE_RADIAL) --ImageReader.single_camera arg (=0) --ImageReader.camera_params arg --ImageReader.default_focal_length_factor arg (=1.2) --SiftExtraction.num_threads arg (=-1) --SiftExtraction.use_gpu arg (=1) --SiftExtraction.gpu_index arg (=-1) --SiftExtraction.max_image_size arg (=3200) --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
The following list briefly documents the functionality of each command, that is
gui: The graphical user interface, see Graphical User Interface for more information.
automatic_reconstruction: Automatically reconstruct sparse and dense model for a set of input images.
project_generator: Generate project files at different quality settings.
feature_importer: Perform feature extraction or import features for a set of images.
matches_importer: Perform feature matching after performing feature extraction.
mapper: Sparse 3D reconstruction / mapping of the dataset using SfM after performing feature extraction and matching.
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_undistorterto initialize the workspace.
stereo_fusion: Fusion of
patch_match_stereoresults 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.
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
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_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_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_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_merger: Attempt to merge two disconnected reconstructions, if they have common registered images.
color_extractor: Extract mean colors for all 3D points of a model.
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.
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
mappercan be visualized via the COLMAP GUI by importing the following files: choose
File > Import Modeland select the folder where the three files,
The dense point cloud obtained with the
stereo_fusioncan be visualized via the COLMAP GUI by importing
File > Import Model from...and then select the file
The dense mesh model
meshed-*.plyobtained with the
delaunay_meshercan currently not be visualized with COLMAP, instead you can use an external viewer, such as Meshlab.