Frequently Asked Questions

Adjusting the options for different reconstruction scenarios and output quality

COLMAP provides many options that can be tuned for different reconstruction scenarios and to trade off accuracy and completeness versus efficiency. The default options are set to for medium to high quality reconstruction of unstructured input data. There are several presets for different scenarios and quality levels, which can be set in the GUI as Extras > Set options for .... To use these presets from the command-line, you can save the current set of options as File > Save project after choosing the presets. The resulting project file can be opened with a text editor to view the different options. Alternatively, you can generate the project file also from the command-line by running colmap project_generator.

Extending COLMAP

If you need to simply analyze the produced sparse or dense reconstructions from COLMAP, you can load the sparse models in Python and Matlab using the provided scripts in scripts/python and scripts/matlab.

If you want to write a C/C++ executable that builds on top of COLMAP, there are two possible approaches. First, the COLMAP headers and library are installed to the CMAKE_INSTALL_PREFIX by default. Compiling against COLMAP as a library is described here. Alternatively, you can start from the src/tools/example.cc code template and implement the desired functionality directly as a new binary within COLMAP.

Fix intrinsics

By default, COLMAP tries to refine the intrinsic camera parameters (except principal point) automatically during the reconstruction. Usually, if there are enough images in the dataset and you share the intrinsics between multiple images, the estimated intrinsic camera parameters in SfM should be better than parameters manually obtained with a calibration pattern.

However, sometimes COLMAP’s self-calibration routine might converge in degenerate parameters, especially in case of the more complex camera models with many distortion parameters. If you know the calibration parameters a priori, you can fix different parameter groups during the reconstruction. Choose Reconstruction > Reconstruction options > Bundle Adj. > refine_* and check which parameter group to refine or to keep constant. Even if you keep the parameters constant during the reconstruction, you can refine the parameters in a final global bundle adjustment by setting Reconstruction > Bundle adj. options > refine_* and then running Reconstruction > Bundle adjustment.

Increase number of matches / sparse 3D points

To increase the number of matches, you should use the more discriminative DSP-SIFT features instead of plain SIFT and also estimate the affine feature shape using the options: --SiftExtraction.estimate_affine_shape=true and --SiftExtraction.domain_size_pooling=true. In addition, you should enable guided feature matching using: --SiftMatching.guided_matching=true.

By default, COLMAP ignores two-view feature tracks in triangulation, resulting in fewer 3D points than possible. Triangulation of two-view tracks can in rare cases improve the stability of sparse image collections by providing additional constraints in bundle adjustment. To also triangulate two-view tracks, unselect the option Reconstruction > Reconstruction options > Triangulation > ignore_two_view_tracks. If your images are taken from far distance with respect to the scene, you can try to reduce the minimum triangulation angle.

Reconstruct sparse/dense model from known camera poses

If the camera poses are known and you want to reconstruct a sparse or dense model of the scene, you must first manually construct a sparse model by creating a cameras.txt, points3D.txt, and images.txt under a new folder:

+── path/to/manually/created/sparse/model
│   +── cameras.txt
│   +── images.txt
│   +── points3D.txt

The points3D.txt file should be empty while every other line in the images.txt should also be empty, since the sparse features are computed, as described below. You can refer to this article for more information about the structure of a sparse model.

Example of images.txt:

0.695104 0.718385 -0.024566 0.012285 -0.046895 0.005253 -0.199664 1 image0001.png
# Make sure every other line is left empty
0.696445 0.717090 -0.023185 0.014441 -0.041213 0.001928 -0.134851 2 image0002.png

0.697457 0.715925 -0.025383 0.018967 -0.054056 0.008579 -0.378221 1 image0003.png

0.698777 0.714625 -0.023996 0.021129 -0.048184 0.004529 -0.313427 2 image0004.png

Each image above must have the same image_id (first column) as in the database (next step). This database can be inspected either in the GUI (under Database management > Processing), or, one can create a reconstruction with colmap and later export it as text in order to see the images.txt file it creates.

To reconstruct a sparse map, you first have to recompute features from the images of the known camera poses as follows:

colmap feature_extractor \
    --database_path $PROJECT_PATH/database.db \
    --image_path $PROJECT_PATH/images

If your known camera intrinsics have large distortion coefficients, you should now manually copy the parameters from your cameras.txt to the database, such that the matcher can leverage the intrinsics. Modifying the database is possible in many ways, but an easy option is to use the provided scripts/python/database.py script. Otherwise, you can skip this step and simply continue as follows:

colmap exhaustive_matcher \ # or alternatively any other matcher
    --database_path $PROJECT_PATH/database.db

colmap point_triangulator \
    --database_path $PROJECT_PATH/database.db \
    --image_path $PROJECT_PATH/images
    --input_path path/to/manually/created/sparse/model \
    --output_path path/to/triangulated/sparse/model

Note that the sparse reconstruction step is not necessary in order to compute a dense model from known camera poses. Assuming you computed a sparse model from the known camera poses, you can compute a dense model as follows:

colmap image_undistorter \
    --image_path $PROJECT_PATH/images \
    --input_path path/to/triangulated/sparse/model \
    --output_path path/to/dense/workspace

colmap patch_match_stereo \
    --workspace_path path/to/dense/workspace

colmap stereo_fusion \
    --workspace_path path/to/dense/workspace \
    --output_path path/to/dense/workspace/fused.ply

Alternatively, you can also produce a dense model without a sparse model as:

colmap image_undistorter \
    --image_path $PROJECT_PATH/images \
    --input_path path/to/manually/created/sparse/model \
    --output_path path/to/dense/workspace

Since the sparse point cloud is used to automatically select neighboring images during the dense stereo stage, you have to manually specify the source images, as described here. The dense stereo stage now also requires a manual specification of the depth range:

colmap patch_match_stereo \
    --workspace_path path/to/dense/workspace \
    --PatchMatchStereo.depth_min $MIN_DEPTH \
    --PatchMatchStereo.depth_max $MAX_DEPTH

colmap stereo_fusion \
    --workspace_path path/to/dense/workspace \
    --output_path path/to/dense/workspace/fused.ply

Merge disconnected models

Sometimes COLMAP fails to reconstruct all images into the same model and hence produces multiple sub-models. If those sub-models have common registered images, they can be merged into a single model as post-processing step:

colmap model_merger \
    --input_path1 /path/to/sub-model1 \
    --input_path2 /path/to/sub-model2 \
    --output_path /path/to/merged-model

To improve the quality of the alignment between the two sub-models, it is recommended to run another global bundle adjustment after the merge:

colmap bundle_adjuster \
    --input_path /path/to/merged-model \
    --output_path /path/to/refined-merged-model

Geo-registration

Geo-registration of models is possible by providing the 3D locations for the camera centers of a subset or all registered images. The 3D similarity transformation between the reconstructed model and the target coordinate frame of the geo-registration is determined from these correspondences.

The geo-registered 3D coordinates can either be extracted from the database (tvec_prior field) or from a user specified text file. For text-files, the geo-registered 3D coordinates of the camera centers for images must be specified with the following format:

image_name1.jpg X1 Y1 Z1
image_name2.jpg X2 Y2 Z2
image_name3.jpg X3 Y3 Z3
...

The coordinates can be either GPS-based (lat/lon/alt) or cartesian-based (x/y/z). In case of GPS coordinates, a conversion will be performed to turn those into cartesian coordinates. The conversion can be done from GPS to ECEF (Earth-Centered-Earth-Fixed) or to ENU (East-North-Up) coordinates. If ENU coordinates are used, the first image GPS coordinates will define the origin of the ENU frame. It is also possible to use ECEF coordinates for alignment and then rotate the aligned reconstruction into the ENU plane.

Note that at least 3 images must be specified to estimate a 3D similarity transformation. Then, the model can be geo-registered using:

colmap model_aligner \
    --input_path /path/to/model \
    --output_path /path/to/geo-registered-model \
    --ref_images_path /path/to/text-file (or --database_path /path/to/database.db) \
    --ref_is_gps 1 \
    --alignment_type ecef \
    --alignment_max_error 3.0 (where 3.0 is the error threshold to be used in RANSAC)

A 3D similarity transformation will be estimated with a RANSAC estimator to be robust to potential outliers in the data. It is required to provide the error threshold to be used in the RANSAC estimator.

Manhattan world alignment

COLMAP has functionality to align the coordinate axes of a reconstruction using a Manhattan world assumption, i.e. COLMAP can automatically determine the gravity axis and the major horizontal axis of the Manhattan world through vanishing point detection in the images. Please, refer to the model_orientation_aligner for more details.

Mask image regions

COLMAP supports masking of keypoints during feature extraction two different ways:

1. Passing mask_path to a folder with image masks. For a given image, the corresponding mask must have the same sub-path below this root as the image has below image_path. The filename must be equal, aside from the added extension .png. For example, for an image image_path/abc/012.jpg, the mask would be mask_path/abc/012.jpg.png.

Passing camera_mask_path to a single mask image. This single mask is applied to all images.

In both cases no features will be extracted in regions, where the mask image is black (pixel intensity value 0 in grayscale).

Register/localize new images into an existing reconstruction

If you have an existing reconstruction of images and want to register/localize new images within this reconstruction, you can follow these steps:

colmap feature_extractor \
    --database_path $PROJECT_PATH/database.db \
    --image_path $PROJECT_PATH/images \
    --image_list_path /path/to/image-list.txt

colmap vocab_tree_matcher \
    --database_path $PROJECT_PATH/database.db \
    --VocabTreeMatching.vocab_tree_path /path/to/vocab-tree.bin \
    --VocabTreeMatching.match_list_path /path/to/image-list.txt

colmap image_registrator \
    --database_path $PROJECT_PATH/database.db \
    --input_path /path/to/existing-model \
    --output_path /path/to/model-with-new-images

colmap bundle_adjuster \
    --input_path /path/to/model-with-new-images \
    --output_path /path/to/model-with-new-images

Note that this first extracts features for the new images, then matches them to the existing images in the database, and finally registers them into the model. The image list text file contains a list of images to extract and match, specified as one image file name per line. The bundle adjustment is optional.

If you need a more accurate image registration with triangulation, then you should restart or continue the reconstruction process rather than just registering the images to the model. Instead of running the image_registrator, you should run the mapper to continue the reconstruction process from the existing model:

colmap mapper \
    --database_path $PROJECT_PATH/database.db \
    --image_path $PROJECT_PATH/images \
    --input_path /path/to/existing-model \
    --output_path /path/to/model-with-new-images

Or, alternatively, you can start the reconstruction from scratch:

colmap mapper \
    --database_path $PROJECT_PATH/database.db \
    --image_path $PROJECT_PATH/images \
    --output_path /path/to/model-with-new-images

Note that dense reconstruction must be re-run from scratch after running the mapper or the bundle_adjuster, as the coordinate frame of the model can change during these steps.

Available functionality without GPU/CUDA

If you do not have a CUDA-enabled GPU but some other GPU, you can use all COLMAP functionality except the dense reconstruction part. However, you can use external dense reconstruction software as an alternative, as described in the Tutorial. If you have a GPU with low compute power or you want to execute COLMAP on a machine without an attached display and without CUDA support, you can run all steps on the CPU by specifying the appropriate options (e.g., --SiftExtraction.use_gpu=false for the feature extraction step). But note that this might result in a significant slow-down of the reconstruction pipeline. Please, also note that feature extraction on the CPU can consume excessive RAM for large images in the default settings, which might require manually reducing the maximum image size using --SiftExtraction.max_image_size and/or setting --SiftExtraction.first_octave 0 or by manually limiting the number of threads using --SiftExtraction.num_threads.

Multi-GPU support in feature extraction/matching

You can run feature extraction/matching on multiple GPUs by specifying multiple indices for CUDA-enabled GPUs, e.g., --SiftExtraction.gpu_index=0,1,2,3 and --SiftMatching.gpu_index=0,1,2,3 runs the feature extraction/matching on 4 GPUs in parallel. Note that you can only run one thread per GPU and this typically also gives the best performance. By default, COLMAP runs one feature extraction/matching thread per CUDA-enabled GPU and this usually gives the best performance as compared to running multiple threads on the same GPU.

Feature matching fails due to illegal memory access

If you encounter the following error message:

MultiplyDescriptor: an illegal memory access was encountered

or the following:

ERROR: Feature matching failed. This probably caused by insufficient GPU
memory. Consider reducing the maximum number of features.

during feature matching, your GPU runs out of memory. Try decreasing the option --SiftMatching.max_num_matches until the error disappears. Note that this might lead to inferior feature matching results, since the lower-scale input features will be clamped in order to fit them into GPU memory. Alternatively, you could change to CPU-based feature matching, but this can become very slow, or better you buy a GPU with more memory.

The maximum required GPU memory can be approximately estimated using the following formula: 4 * num_matches * num_matches + 4 * num_matches * 256. For example, if you set --SiftMatching.max_num_matches 10000, the maximum required GPU memory will be around 400MB, which are only allocated if one of your images actually has that many features.

Trading off completeness and accuracy in dense reconstruction

If the dense point cloud contains too many outliers and too much noise, try to increase the value of option --StereoFusion.min_num_pixels.

If the reconstructed dense surface mesh model using Poisson reconstruction contains no surface or there are too many outlier surfaces, you should reduce the value of option --PoissonMeshing.trim to decrease the surface are and vice versa to increase it. Also consider to try the reduce the outliers or increase the completeness in the fusion stage, as described above.

If the reconstructed dense surface mesh model using Delaunay reconstruction contains too noisy or incomplete surfaces, you should increase the --DenaunayMeshing.quality_regularization parameter to obtain a smoother surface. If the resolution of the mesh is too coarse, you should reduce the --DelaunayMeshing.max_proj_dist option to a lower value.

Improving dense reconstruction results for weakly textured surfaces

For scenes with weakly textured surfaces it can help to have a high resolution of the input images (--PatchMatchStereo.max_image_size) and a large patch window radius (--PatchMatchStereo.window_radius). You may also want to reduce the filtering threshold for the photometric consistency cost (--PatchMatchStereo.filter_min_ncc).

Surface mesh reconstruction

COLMAP supports two types of surface reconstruction algorithms. Poisson surface reconstruction [kazhdan2013] and graph-cut based surface extraction from a Delaunay triangulation. Poisson surface reconstruction typically requires an almost outlier-free input point cloud and it often produces bad surfaces in the presence of outliers or large holes in the input data. The Delaunay triangulation based meshing algorithm is more robust to outliers and in general more scalable to large datasets than the Poisson algorithm, but it usually produces less smooth surfaces. Furthermore, the Delaunay based meshing can be applied to sparse and dense reconstruction results. To increase the smoothness of the surface as a post-processing step, you could use Laplacian smoothing, as e.g. implemented in Meshlab.

Note that the two algorithms can also be combined by first running the Delaunay meshing to robustly filter outliers from the sparse or dense point cloud and then, in the second step, performing Poisson surface reconstruction to obtain a smooth surface.

Speedup dense reconstruction

The dense reconstruction can be speeded up in multiple ways:

Put more GPUs in your system as the dense reconstruction can make use of multiple GPUs during the stereo reconstruction step. Put more RAM into your system and increase the --PatchMatchStereo.cache_size, --StereoFusion.cache_size to the largest possible value in order to speed up the dense fusion step.
Do not perform geometric dense stereo reconstruction --PatchMatchStereo.geom_consistency false. Make sure to also enable --PatchMatchStereo.filter true in this case.
Reduce the --PatchMatchStereo.max_image_size, --StereoFusion.max_image_size values to perform dense reconstruction on a maximum image resolution.
Reduce the number of source images per reference image to be considered, as described here.
Increase the patch windows step --PatchMatchStereo.window_step to 2.
Reduce the patch window radius --PatchMatchStereo.window_radius.
Reduce the number of patch match iterations --PatchMatchStereo.num_iterations.
Reduce the number of sampled views --PatchMatchStereo.num_samples.
To speedup the dense stereo and fusion step for very large reconstructions, you can use CMVS to partition your scene into multiple clusters and to prune redundant images, as described here.

Note that apart from upgrading your hardware, the proposed changes might degrade the quality of the dense reconstruction results. When canceling the stereo reconstruction process and restarting it later, the previous progress is not lost and any already processed views will be skipped.

Reduce memory usage during dense reconstruction

If you run out of GPU memory during patch match stereo, you can either reduce the maximum image size by setting the option --PatchMatchStereo.max_image_size or reduce the number of source images in the stereo/patch-match.cfg file from e.g. __auto__, 30 to __auto__, 10. Note that enabling the geom_consistency option increases the required GPU memory.

If you run out of CPU memory during stereo or fusion, you can reduce the --PatchMatchStereo.cache_size or --StereoFusion.cache_size specified in gigabytes or you can reduce --PatchMatchStereo.max_image_size or --StereoFusion.max_image_size. Note that a too low value might lead to very slow processing and heavy load on the hard disk.

For large-scale reconstructions of several thousands of images, you should consider splitting your sparse reconstruction into more manageable clusters of images using e.g. CMVS [furukawa10]. In addition, CMVS allows to prune redundant images observing the same scene elements. Note that, for this use case, COLMAP’s dense reconstruction pipeline also supports the PMVS/CMVS folder structure when executed from the command-line. Please, refer to the workspace folder for example shell scripts. Note that the example shell scripts for PMVS/CMVS are only generated, if the output type is set to PMVS. Since CMVS produces highly overlapping clusters, it is recommended to increase the default value of 100 images per cluster to as high as possible according to your available system resources and speed requirements. To change the number of images using CMVS, you must modify the shell scripts accordingly. For example, cmvs pmvs/ 500 to limit each cluster to 500 images. If you want to use CMVS to prune redundant images but not to cluster the scene, you can simply set this number to a very large value.

Manual specification of source images during dense reconstruction

You can change the number of source images in the stereo/patch-match.cfg file from e.g. __auto__, 30 to __auto__, 10. This selects the images with the most visual overlap automatically as source images. You can also use all other images as source images, by specifying __all__. Alternatively, you can manually specify images with their name, for example:

image1.jpg
image2.jpg, image3.jpg
image2.jpg
image1.jpg, image3.jpg
image3.jpg
image1.jpg, image2.jpg

Here, image2.jpg and image3.jpg are used as source images for image1.jpg, etc.

Multi-GPU support in dense reconstruction

You can run dense reconstruction on multiple GPUs by specifying multiple indices for CUDA-enabled GPUs, e.g., --PatchMatchStereo.gpu_index=0,1,2,3 runs the dense reconstruction on 4 GPUs in parallel. You can also run multiple dense reconstruction threads on the same GPU by specifying the same GPU index twice, e.g., --PatchMatchStereo.gpu_index=0,0,1,1,2,3. By default, COLMAP runs one dense reconstruction thread per CUDA-enabled GPU.

Fix GPU freezes and timeouts during dense reconstruction

The stereo reconstruction pipeline runs on the GPU using CUDA and puts the GPU under heavy load. You might experience a display freeze or even a program crash during the reconstruction. As a solution to this problem, you could use a secondary GPU in your system, that is not connected to your display by setting the GPU indices explicitly (usually index 0 corresponds to the card that the display is attached to). Alternatively, you can increase the GPU timeouts of your system, as detailed in the following.

By default, the Windows operating system detects response problems from the GPU, and recovers to a functional desktop by resetting the card and aborting the stereo reconstruction process. The solution is to increase the so-called “Timeout Detection & Recovery” (TDR) delay to a larger value. Please, refer to the NVIDIA Nsight documentation or to the Microsoft documentation on how to increase the delay time under Windows. You can increase the delay using the following Windows Registry entries:

[HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\GraphicsDrivers]
"TdrLevel"=dword:00000001
"TdrDelay"=dword:00000120

To set the registry entries, execute the following commands using administrator privileges (e.g., in cmd.exe or powershell.exe):

reg add HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\GraphicsDrivers /v TdrLevel /t REG_DWORD /d 00000001
reg add HKEY_LOCAL_MACHINE\SYSTEM\CurrentControlSet\Control\GraphicsDrivers /v TdrDelay /t REG_DWORD /d 00000120

and restart your machine afterwards to make the changes effective.

The X window system under Linux/Unix has a similar feature and detects response problems of the GPU. The easiest solution to avoid timeout problems under the X window system is to shut it down and run the stereo reconstruction from the command-line. Under Ubuntu, you could first stop X using:

sudo service lightdm stop

And then run the dense reconstruction code from the command-line:

colmap patch_match_stereo ...

Finally, you can restart your desktop environment with the following command:

sudo service lightdm start

If the dense reconstruction still crashes after these changes, the reason is probably insufficient GPU memory, as discussed in a separate item in this list.