## 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:

./src/exe/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:

./src/exe/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 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:

./src/exe/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:

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

./src/exe/vocab_tree_matcher \
--database_path $PROJECT_PATH/database.db \ --VocabTreeMatching.vocab_tree_path /path/to/vocab-tree.bin \ --VocabTreeMatching.match_list_path /path/to/image-list.txt ./src/exe/image_registrator \ --database_path$PROJECT_PATH/database.db \
--image_path \$PROJECT_PATH/images \
--import_path /path/to/existing-model \
--export_path /path/to/model-with-new-images


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.

## Multi-GPU support in feature matching¶

You can run feature matching on multiple GPUs by specifying multiple indices for CUDA-enabled GPUs, e.g., --SiftMatching.gpu_index=0,1,2,3 runs the feature matching on 4 GPUs in parallel. By default, COLMAP runs feature matching on all CUDA-enabled GPUs.

## Feature matching fails due to illegal memory access¶

If you encounter the following error message:

MultiplyDescriptor: an illegal memory access was encountered


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.

## Reduce memory usage during dense reconstruction¶

If you run out of GPU memory during patch match stereo, you can either reduce the maximum image resolution by setting the option 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 fusion, you can reduce the --DenseFusion.cache_size. Note that a too low value might lead to very slow fusion 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]. 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. 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. Since CMVS produces highly overlapping clusters, it is recommended to increase the default value of 100 images to as high as possible according to your available system resources.

## 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:

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., --DenseStereo.gpu_index=0,1,2,3 runs the dense reconstruction on 4 GPUs in parallel. By default, COLMAP runs dense reconstruction on all CUDA-enabled GPUs.

## 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. 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.

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:

./src/exe/dense_stereo ...


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

sudo service lightdm start