Frequently Asked Questions

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, the easiest approach is to start from the src/tools/ code template and implement the desired functionality in a new binary.

Share intrinsics

COLMAP supports shared intrinsics for arbitrary groups of images and camera models. Images share the same intrinsics, if they refer to the same camera, as specified by the camera_id property in the database. You can add new cameras and set shared intrinsics in the database management tool. Please, refer to Database Management for more information.

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.

Principal point refinement

By default, COLMAP keeps the principal point constant during the reconstruction, as principal point estimation is an ill-posed problem in general. Once all images are reconstructed, the problem is most often constrained enough that you can try to refine the principal point in global bundle adjustment, especially when sharing intrinsic parameters between multiple images. Please, refer to Fix intrinsics for more information.

Increase number of sparse 3D points

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.

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 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 of the camera centers for images must be specified in a text-file with the following format:

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

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

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.

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 \
    --image_path $PROJECT_PATH/images \
    --import_path /path/to/existing-model \
    --export_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 \
    --import_path /path/to/existing-model \
    --export_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 \
    --export_path /path/to/model-with-new-images

Note that dense reconstruction must be re-run from scratch after adding new images, as the coordinate frame of the model is changed.

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 not that this might result in a significant slow-down of the reconstruction pipeline.

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 you use a GPU with more memory.

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 --DenseFusion.min_num_pixels.

If the reconstructed dense surface mesh model contains no surface or there are too many outlier surfaces, you should reduce the value of option --DenseMeshing.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.

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 (--DenseStereo.max_image_size) and a large patch window radius (--DenseStereo.window_radius). You may also want to reduce the filtering threshold for the photometric consistency cost (--DenseStereo.filter_min_ncc).

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 --DenseStereo.cache_size, --DenseFusion.cache_size to the largest possible value in order to speed up the dense fusion step.
  • Do not perform geometric dense stereo reconstruction --DenseStereo.geom_consistency false. Make sure to also enable --DenseStereo.filter true in this case.
  • Reduce the --DenseStereo.max_image_size, --DenseFusion.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.
  • Reduce the patch window radius --DenseStereo.window_radius.
  • Reduce the number of patch match iterations --DenseStereo.num_iterations.
  • Reduce the number of sampled views --DenseStereo.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 --DenseStereo.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 --DenseStereo.cache_size or --DenseFusion.cache_size specified in gigabytes or you can reduce --DenseStereo.max_image_size or --DenseFusion.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. 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. Alternatively, you can manually specify images with their name, for example:

image2.jpg, image3.jpg
image1.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., --DenseStereo.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., --DenseStereo.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:


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 dense_stereo ...

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

sudo service lightdm start