.. _tutorial:

Tutorial
========

This tutorial covers the topic of image-based 3D reconstruction by demonstrating
the individual processing steps in COLMAP. If you are interested in a more
general and mathematical introduction to the topic of image-based 3D
reconstruction, please also refer to the `CVPR 2017 Tutorial on Large-scale 3D
Modeling from Crowdsourced Data <https://demuc.de/tutorials/cvpr2017/>`_ and
[schoenberger_thesis]_.

Image-based 3D reconstruction from images traditionally first recovers a sparse
representation of the scene and the camera poses of the input images using
Structure-from-Motion. This output then serves as the input to Multi-View Stereo
to recover a dense representation of the scene.


.. _quick-start:

Quickstart
----------

First, start the graphical user interface of COLMAP, as described :ref:`here
<gui>`. COLMAP provides an automatic reconstruction tool that simply takes
a folder of input images and produces a sparse and dense reconstruction in a
workspace folder. Click ``Reconstruction > Automatic Reconstruction`` in the GUI
and specify the relevant options. The output is written to the workspace folder.
For example, if your images are located in ``path/to/project/images``, you could
select ``path/to/project`` as a workspace folder and after running the automatic
reconstruction tool, the folder would look similar to this::

    +── images
    │   +── image1.jpg
    │   +── image2.jpg
    │   +── ...
    +── sparse
    │   +── 0
    │   │   +── cameras.bin
    │   │   +── images.bin
    │   │   +── points3D.bin
    │   +── ...
    +── dense
    │   +── 0
    │   │   +── images
    │   │   +── sparse
    │   │   +── stereo
    │   │   +── fused.ply
    │   │   +── meshed-poisson.ply
    │   │   +── meshed-delaunay.ply
    │   +── ...
    +── database.db

Here, the ``path/to/project/sparse`` contains the sparse models for all
reconstructed components, while ``path/to/project/dense`` contains their
corresponding dense models. The dense point cloud ``fused.ply`` can be imported
in COLMAP using ``File > Import model from ...``, while the dense mesh must be
visualized with an external viewer such as Meshlab.

The following sections give general recommendations and describe the
reconstruction process in more detail, if you need more control over the
reconstruction process/parameters or if you are interested in the underlying
technology in COLMAP.


Structure-from-Motion
---------------------

.. figure:: images/incremental-sfm.png
    :alt: Incremental Structure-from-Motion pipeline
    :figclass: align-center

    COLMAP's incremental Structure-from-Motion pipeline.

Structure-from-Motion (SfM) is the process of reconstructing 3D structure from
its projections into a series of images. The input is a set of overlapping
images of the same object, taken from different viewpoints. The output is a 3-D
reconstruction of the object, and the reconstructed intrinsic and extrinsic
camera parameters of all images. Typically, Structure-from-Motion systems divide
this process into three stages:

1) Feature detection and extraction
2) Feature matching and geometric verification
3) Structure and motion reconstruction

COLMAP reflects these stages in different modules, that can be combined
depending on the application. More information on Structure-from-Motion in
general and the algorithms in COLMAP can be found in [schoenberger16sfm]_ and
[schoenberger16mvs]_.

If you have control over the picture capture process, please follow these
guidelines for optimal reconstruction results:

- Capture images with **good texture**. Avoid completely texture-less images
  (e.g., a white wall or empty desk). If the scene does not contain enough
  texture itself, you could place additional background objects, such as
  posters, etc.

- Capture images at **similar illumination** conditions. Avoid high dynamic
  range scenes (e.g., pictures against the sun with shadows or pictures
  through doors/windows). Avoid specularities on shiny surfaces.

- Capture images with **high visual overlap**. Make sure that each object is
  seen in at least 3 images -- the more images the better.

- Capture images from **different viewpoints**. Do not take images from the
  same location by only rotating the camera, e.g., make a few steps after each
  shot. At the same time, try to have enough images from a relatively similar
  viewpoint. Note that more images is not necessarily better and might lead to a
  slow reconstruction process. If you use a video as input, consider
  down-sampling the frame rate.


Multi-View Stereo
-----------------

Multi-View Stereo (MVS) takes the output of SfM to compute depth and/or normal
information for every pixel in an image. Fusion of the depth and normal maps of
multiple images in 3D then produces a dense point cloud of the scene. Using the
depth and normal information of the fused point cloud, algorithms such as the
(screened) Poisson surface reconstruction [kazhdan2013]_ can then recover the 3D
surface geometry of the scene. More information on Multi-View Stereo in general
and the algorithms in COLMAP can be found in [schoenberger16mvs]_.


Preface
-------

COLMAP requires only few steps to do a standard reconstruction for a general
user. For more experienced users, the program exposes many different parameters,
only some of which are intuitive to a beginner. The program should usually work
without the need to modify any parameters. The defaults are chosen as a trade-
off between reconstruction robustness/quality and speed. You can set "optimal"
options for different reconstruction scenarios by choosing ``Extras > Set
options for ... data``. If in doubt what settings to choose, stick to the
defaults. The source code contains more documentation about all parameters.

COLMAP is research software and in rare cases it may exit ungracefully if some
constraints are not fulfilled. In this case, the program prints a traceback to
stdout. To see this traceback or more debug information, it is recommended to
run the executables (including the GUI) from the command-line, where you can
define various levels of logging verbosity.


Terminology
-----------

The term **camera** is associated with the physical object of a camera using the
same zoom-factor and lens. A camera defines the intrinsic projection model in
COLMAP. A single camera can take multiple images with the same resolution,
intrinsic parameters, and distortion characteristics. The term **image** is
associated with a bitmap file, e.g., a JPEG or PNG file on disk. COLMAP detects
**keypoints** in each image whose appearance is described by numerical
**descriptors**. Pure appearance-based correspondences between
keypoints/descriptors are defined by **matches**, while **inlier matches** are
geometrically verified and used for the reconstruction procedure.


Data Structure
--------------

COLMAP assumes that all input images are in one input directory with potentially
nested sub-directories. It recursively considers all images stored in this
directory, and it supports various different image formats (see `FreeImage
<http://freeimage.sourceforge.net/documentation.html>`_). Other files are
automatically ignored. If high performance is a requirement, then you should
separate any files that are not images. Images are identified uniquely by their
relative file path. For later processing, such as image undistortion or dense
reconstruction, the relative folder structure should be preserved. COLMAP does
not modify the input images or directory and all extracted data is stored in a
single, self-contained SQLite database file (see :doc:`database`).

The first step is to start the graphical user interface of COLMAP by running the
pre-built binaries (Windows: `COLMAP.bat`, Mac: `COLMAP.app`) or by executing
``./src/colmap/exe/colmap gui`` from the CMake build folder. Next, create a new project
by choosing ``File > New project``. In this dialog, you must select where to
store the database and the folder that contains the input images. For
convenience, you can save the entire project settings to a configuration file by
choosing ``File > Save project``. The project configuration stores the absolute
path information of the database and image folder in addition to any other
parameter settings. If you decide to move the database or image folder, you must
change the paths accordingly by creating a new project. Alternatively, the
resulting `.ini` configuration file can be directly modified in a text editor of
your choice. To reopen an existing project, you can simply open the
configuration file by choosing ``File > Open project`` and all parameter
settings should be recovered. Note that all COLMAP executables can be started
from the command-line by either specifying individual settings as command-line
arguments or by providing the path to the project configuration file (see
:ref:`Interface <interface>`).

An example folder structure could look like this::

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

In this example, you would select `/path/to/project/images` as the image folder
path, `/path/to/project/database.db` as the database file path, and save the
project configuration to `/path/to/project/project.ini`.


Feature Detection and Extraction
--------------------------------

In the first step, feature detection/extraction finds sparse feature points in
the image and describes their appearance using a numerical descriptor. COLMAP
imports images and performs feature detection/extraction in one step in order to
only load images from disk once.

Next, choose ``Processing > Extract features``. In this dialog, you must first
decide on the employed intrinsic camera model. You can either automatically
extract focal length information from the embedded EXIF information or manually
specify intrinsic parameters, e.g., as obtained in a lab calibration. If an
image has partial EXIF information, COLMAP tries to find the missing camera
specifications in a large database of camera models automatically. If all your
images were captured by the same physical camera with identical zoom factor, it
is recommended to share intrinsics between all images. Note that the program
will exit ungracefully if the same camera model is shared among all images but
not all images have the same size or EXIF focal length. If you have several
groups of images that share the same intrinsic camera parameters, you can easily
modify the camera models at a later point as well (see :ref:`Database Management
<database-management>`). If in doubt what to choose in this step, simply stick
to the default parameters.

You can either detect and extract new features from the images or import
existing features from text files. COLMAP extracts SIFT [lowe04]_ features
either on the GPU or the CPU. The GPU version requires an attached display,
while the CPU version is recommended for use on a server. In general, the GPU
version is favorable as it has a customized feature detection mode that often
produces higher quality features in the case of high contrast images. If you
import existing features, every image must have a text file next to it (e.g.,
`/path/to/image1.jpg` and `/path/to/image1.jpg.txt`) in the following format::

    NUM_FEATURES 128
    X Y SCALE ORIENTATION D_1 D_2 D_3 ... D_128
    ...
    X Y SCALE ORIENTATION D_1 D_2 D_3 ... D_128

where `X, Y, SCALE, ORIENTATION` are floating point numbers and `D_1...D_128`
values in the range `0...255`. The file should have `NUM_FEATURES` lines with
one line per feature. For example, if an image has 4 features, then the text
file should look something like this::

    4 128
    1.2 2.3 0.1 0.3 1 2 3 4 ... 21
    2.2 3.3 1.1 0.3 3 2 3 2 ... 32
    0.2 1.3 1.1 0.3 3 2 3 2 ... 2
    1.2 2.3 1.1 0.3 3 2 3 2 ... 3

Note that by convention the upper left corner of an image has coordinate `(0,
0)` and the center of the upper left most pixel has coordinate `(0.5, 0.5)`. If
you must  import features for large image collections, it is much more efficient
to directly access the database with your favorite scripting language (see
:ref:`Database Format <database-format>`).

If you are done setting all options, choose ``Extract`` and wait for the
extraction to finish or cancel. If you cancel during the extraction process, the
next time you start extracting images for the same project, COLMAP automatically
continues where it left off. This also allows you to add images to an existing
project/reconstruction. In this case, be sure to verify the camera parameters
when using shared intrinsics.

All extracted data will be stored in the database file and can be
reviewed/managed in the database management tool (see :ref:`Database Management
<database-management>`) or, for experts, directly modified using SQLite (see
:ref:`Database Format <database-format>`).


Feature Matching and Geometric Verification
-------------------------------------------

In the second step, feature matching and geometric verification finds
correspondences between the feature points in different images.

Please, choose ``Processing > Match features`` and select one of the provided
matching modes, that are intended for different input scenarios:

- **Exhaustive Matching**: If the number of images in your dataset is
  relatively low (up to several hundreds), this matching mode should be fast
  enough and leads to the best reconstruction results. Here, every image is
  matched against every other image, while the block size determines how many
  images are loaded from disk into memory at the same time.

- **Sequential Matching**: This mode is useful if the images are acquired in
  sequential order, e.g., by a video camera. In this case, consecutive frames
  have visual overlap and there is no need to match all image pairs
  exhaustively. Instead, consecutively captured images are matched against
  each other. This matching mode has built-in loop detection based on a
  vocabulary tree, where every N-th image (`loop_detection_period`) is matched
  against its visually most similar images (`loop_detection_num_images`). Note
  that image file names must be ordered sequentially (e.g., `image0001.jpg`,
  `image0002.jpg`, etc.). The order in the database is not relevant, since the
  images are explicitly ordered according to their file names. Note that loop
  detection requires a pre-trained vocabulary tree, that can be downloaded
  from https://demuc.de/colmap/.

- **Vocabulary Tree Matching**: In this matching mode [schoenberger16vote]_,
  every image is matched against its visual nearest neighbors using a vocabulary
  tree with spatial re-ranking. This is the recommended matching mode for large
  image collections (several thousands). This requires a pre-trained vocabulary
  tree, that can be downloaded from https://demuc.de/colmap/.

- **Spatial Matching**: This matching mode matches every image against its
  spatial nearest neighbors. Spatial locations can be manually set in the
  database management. By default, COLMAP also extracts GPS information from
  EXIF and uses it for spatial nearest neighbor search. If accurate prior
  location information is available, this is the recommended matching mode.

- **Transitive Matching**: This matching mode uses the transitive relations of
  already existing feature matches to produce a more complete matching graph.
  If an image A matches to an image B and B matches to C, then this matcher
  attempts to match A to C directly.

- **Custom Matching**: This mode allows to specify individual image pairs for
  matching or to import individual feature matches. To specify image pairs, you
  have to provide a text file with one image pair per line::

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

  where `image1.jpg` is the relative path in the image folder. You have two
  options to import individual feature matches. Either raw feature matches,
  which are not geometrically verified or already geometrically verified feature
  matches. In both cases, the expected format is::

    image1.jpg image2.jpg
    0 1
    1 2
    3 4
    <empty-line>
    image1.jpg image3.jpg
    0 1
    1 2
    3 4
    4 5
    <empty-line>
    ...

  where `image1.jpg` is the relative path in the image folder and the pairs of
  numbers are zero-based feature indices in the respective images. If you must
  import many matches for large image collections, it is more efficient to
  directly access the database with a scripting language of your choice.

If you are done setting all options, choose ``Match`` and wait for the matching
to finish or cancel in between. Note that this step can take a significant
amount of time depending on the number of images, the number of features per
image, and the chosen matching mode. Expected times for exhaustive matching are
from a few minutes for tens of images to a few hours for hundreds of images to
days or weeks for thousands of images. If you cancel the matching process or
import new images after matching, COLMAP only matches image pairs that have not
been matched previously. The overhead of skipping already matched image pairs is
low. This also enables to match additional images imported after an initial
matching and it enables to combine different matching modes for the same
dataset.

All extracted data will be stored in the database file and can be
reviewed/managed in the database management tool (see :ref:`Database Management
<database-management>`) or, for experts, directly modified using SQLite (see
:ref:`Database Format <database-format>`).

Note that feature matching requires a GPU and that the display performance of
your computer might degrade significantly during the matching process. If your
system has multiple CUDA-enabled GPUs, you can select specific GPUs with the
`gpu_index` option.


Sparse Reconstruction
---------------------

After producing the scene graph in the previous two steps, you can start the
incremental reconstruction process by choosing ``Reconstruction > Start``.
COLMAP first loads all extracted data from the database into memory and seeds
the reconstruction from an initial image pair. Then, the scene is incrementally
extended by registering new images and triangulating new points. The results are
visualized in "real-time" during this reconstruction process. Refer to the
:ref:`Graphical User Interface <gui>` section for more details about the
available controls. COLMAP attempts to reconstruct multiple models if not all
images are registered into the same model. The different models can be selected
from the drop-down menu in the toolbar. If the different models have common
registered images, you can use the ``model_converter`` executable to merge them
into a single reconstruction (see :ref:`FAQ <faq-merge-models>` for details).

Ideally, the reconstruction works fine and all images are registered. If this is
not the case, it is recommended to:

- Perform additional matching. For best results, use exhaustive matching, enable
  guided matching, increase the number of nearest neighbors in vocabulary tree
  matching, or increase the overlap in sequential matching, etc.

- Manually choose an initial image pair, if COLMAP fails to initialize. Choose
  ``Reconstruction > Reconstruction options > Init`` and set images from the
  database management tool that have enough matches from different viewpoints.


Importing and Exporting
-----------------------

COLMAP provides several export options for further processing. For full
flexibility, it is recommended to export the reconstruction in COLMAP's data
format by choosing ``File > Export`` to export the currently viewed model or
``File > Export all`` to export all reconstructed models. The model is exported
in the selected folder using separate text files for the reconstructed cameras,
images, and points. When exporting in COLMAP's data format, you can re- import
the reconstruction for later visualization, image undistortion, or to continue
an existing reconstruction from where it left off (e.g., after importing and
matching new images). To import a model, choose ``File > Import`` and select the
export folder path. Alternatively, you can also export the model in various
other formats, such as Bundler, VisualSfM [#f1]_, PLY, or VRML by choosing
``File > Export as...``. COLMAP can visualize plain PLY point cloud files with
RGB information by choosing ``File > Import From...``. Further information about
the format of the exported models can be found :ref:`here <output-format>`.


.. _dense-reconstruction:

Dense Reconstruction
--------------------

After reconstructing a sparse representation of the scene and the camera poses
of the input images, MVS can now recover denser scene geometry. COLMAP has an
integrated dense reconstruction pipeline to produce depth and normal maps for
all registered images, to fuse the depth and normal maps into a dense point
cloud with normal information, and to finally estimate a dense surface from the
fused point cloud using Poisson [kazhdan2013]_ or Delaunay reconstruction.

To get started, import your sparse 3D model into COLMAP (or select the
reconstructed model after finishing the previous sparse reconstruction steps).
Then, choose ``Reconstruction > Multi-view stereo`` and select an empty or
existing workspace folder, which is used for the output and of all dense
reconstruction results. The first step is to ``undistort`` the images, second to
compute the depth and normal maps using ``stereo``, third to ``fuse`` the depth
and normals maps to a point cloud, followed by a final, optional point cloud
``meshing`` step. During the stereo reconstruction process, the display might
freeze due to heavy compute load and, if your GPU does not have enough memory,
the reconstruction process might ungracefully crash. Please, refer to the FAQ
(:ref:`freeze <faq-dense-timeout>` and :ref:`memory <faq-dense-memory>`) for
information on how to avoid these problems. Note that the reconstructed normals
of the point cloud cannot be directly visualized in COLMAP, but e.g. in Meshlab
by enabling ``Render > Show Normal/Curvature``. Similarly, the reconstructed
dense surface mesh model must be visualized with external software.

In addition to the internal dense reconstruction functionality, COLMAP exports 
to several other dense reconstruction libraries, such as CMVS/PMVS [furukawa10]_ 
or CMP-MVS [jancosek11]_. Please choose ``Extras > Undistort images`` and select 
the appropriate format. The output folders contain the reconstruction and the
undistorted images. In addition, the folders contain sample shell scripts to
perform the dense reconstruction. To run PMVS2, execute the following commands:

    ./path/to/pmvs2 /path/to/undistortion/folder/pmvs/ option-all

where `/path/to/undistortion/folder` is the folder selected in the undistortion
dialog. Make sure not to forget the trailing slash in
`/path/to/undistortion/folder/pmvs/` in the above command-line arguments.

For large datasets, you probably want to first run CMVS to cluster the scene
into more manageable parts and then run COLMAP or PMVS2. Please, refer to the
sample shell scripts in the undistortion output folder on how to run CMVS in
combination with COLMAP or PMVS2. Moreover, there are a number of external
libraries that support COLMAP's output:

- `CMVS/PMVS <http://www.di.ens.fr/pmvs/>`_ [furukawa10]_
- `CMP-MVS <http://ptak.felk.cvut.cz/sfmservice/websfm.pl>`_ [jancosek11]_
- `Line3D++ <https://github.com/manhofer/Line3Dpp>`_ [hofer16]_.


.. _database-management:

Database Management
-------------------

You can review and manage the imported cameras, images, and feature matches in
the database management tool. Choose ``Processing > Manage database``. In the
opening dialog, you can see the list of imported images and cameras. You can
view the features and matches for each image by clicking ``Show image`` and
``Overlapping images``. Individual entries in the database tables can be
modified by double clicking specific cells. Note that any changes to the
database are only effective after clicking ``Save``.

To share intrinsic camera parameters between arbitrary groups of images, select
a single or multiple images, choose ``Set camera`` and set the `camera_id`,
which corresponds to the unique `camera_id` column in the cameras table. You can
also add new cameras with specific parameters. By setting the
`prior_focal_length` flag to 0 or 1, you can give a hint whether the
reconstruction algorithm should trust the focal length value. In case of a prior
lab calibration, you want to set this value to 1. Without prior knowledge about
the focal length, it is recommended to set this value to `1.25 *
max(width_in_px, height_in_px)`.

The database management tool has only limited functionality and, for full
control over the data, you must directly modify the SQLite database (see
:ref:`Database Format <database-format>`). By accessing the database directly,
you can use COLMAP only for feature extraction and matching or you can import
your own features and matches to only use COLMAP's incremental reconstruction
algorithm.


.. _interface:

Graphical and Command-line Interface
------------------------------------

Most of COLMAP's features are accessible from both the graphical and the
command-line interface, which are both embedded in the same executable. You can
provide the options directly as command-line arguments or you can provide a
`.ini` project configuration file containing the options using the
``--project_path path/to/project.ini`` argument. To start the GUI application,
please execute ``colmap gui`` or directly specify a project configuration as
``colmap gui --project_path path/to/project.ini`` to avoid tedious selection in
the GUI. To list the different commands available from the command-line, execute
``colmap help``. For example, to run feature extraction from the command-line,
you must execute ``colmap feature_extractor``. The :ref:`graphical user
interface <gui>` and :ref:`command-line Interface <cli>` sections provide more
details about the available commands.


.. rubric:: Footnotes

.. [#f1] VisualSfM's [wu13]_ projection model applies the distortion to the
    measurements and COLMAP to the projection, hence the exported NVM file is
    not fully compatible with VisualSfM.
