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<!DOCTYPE html>
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<meta property="og:description" content="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 ..." />
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<section id="frequently-asked-questions">
<h1>Frequently Asked Questions<a class="headerlink" href="#frequently-asked-questions" title="Link to this heading">#</a></h1>
<section id="adjusting-the-options-for-different-reconstruction-scenarios-and-output-quality">
<h2>Adjusting the options for different reconstruction scenarios and output quality<a class="headerlink" href="#adjusting-the-options-for-different-reconstruction-scenarios-and-output-quality" title="Link to this heading">#</a></h2>
<p>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 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 <code class="docutils literal notranslate"><span class="pre">Extras</span> <span class="pre">></span> <span class="pre">Set</span> <span class="pre">options</span> <span class="pre">for</span> <span class="pre">...</span></code>.
To use these presets from the command-line, you can save the current set of
options as <code class="docutils literal notranslate"><span class="pre">File</span> <span class="pre">></span> <span class="pre">Save</span> <span class="pre">project</span></code> 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 <code class="docutils literal notranslate"><span class="pre">colmap</span> <span class="pre">project_generator</span></code>.</p>
</section>
<section id="extending-colmap">
<h2>Extending COLMAP<a class="headerlink" href="#extending-colmap" title="Link to this heading">#</a></h2>
<p>If you need to simply analyze the produced sparse or dense reconstructions from
COLMAP, you can load the sparse models using pycolmap in Python or the
scripts in <code class="docutils literal notranslate"><span class="pre">scripts/matlab</span></code> for Matlab.</p>
<p>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 <code class="docutils literal notranslate"><span class="pre">CMAKE_INSTALL_PREFIX</span></code> by default. Compiling against COLMAP as a
library is described <a class="reference internal" href="install.html#installation-library"><span class="std std-ref">here</span></a>. Alternatively, you can
start from the <code class="docutils literal notranslate"><span class="pre">src/colmap/tools/example.cc</span></code> code template and implement the desired
functionality directly as a new binary within COLMAP.</p>
</section>
<section id="choosing-between-sift-and-aliked-features">
<h2>Choosing between SIFT and ALIKED features<a class="headerlink" href="#choosing-between-sift-and-aliked-features" title="Link to this heading">#</a></h2>
<p>COLMAP supports two feature extraction algorithms: SIFT (default) and ALIKED
(requires ONNX support). Here are some guidelines for choosing between them:</p>
<ul class="simple">
<li><p><strong>SIFT</strong> is the most widely tested and robust choice. It works well for
scenarios with moderate to high view overlap, sufficient scene texture,
and captured under similar illumination conditions. It supports both GPU
and CPU extraction.</p></li>
<li><p><strong>ALIKED</strong> is a learned feature extractor that can produce more repeatable
features in some cases, particularly for scenes with limited view overlap,
little scene texture, and drastic illumination changes. It requires ONNX
Runtime at build time (<code class="docutils literal notranslate"><span class="pre">-DONNX_ENABLED=ON</span></code>).</p></li>
</ul>
<p>Both feature types support brute-force matching as well as LightGlue neural
network-based matching. LightGlue typically produces higher inlier ratios,
especially for image pairs with large viewpoint or illumination changes, but
requires ONNX support. See <a class="reference internal" href="features.html#features"><span class="std std-ref">Feature Extraction and Matching</span></a>
for details on available options.</p>
<p>Do not mix different feature types (e.g., SIFT and ALIKED) in the same
database, as the descriptors are incompatible.</p>
</section>
<section id="choosing-the-right-camera-model">
<span id="faq-choosing-camera-model"></span><h2>Choosing the right camera model<a class="headerlink" href="#choosing-the-right-camera-model" title="Link to this heading">#</a></h2>
<p>COLMAP supports many camera models with varying numbers of parameters (see
<a class="reference internal" href="cameras.html"><span class="doc">Camera Models</span></a> for the full list). Choosing the right model depends on your
lens type and reconstruction requirements:</p>
<ul class="simple">
<li><p><strong>SIMPLE_RADIAL</strong> (default): A good starting point for most standard cameras.
Models a single focal length, principal point, and one radial distortion
parameter.</p></li>
<li><p><strong>PINHOLE</strong>: Use if your images have negligible lens distortion (e.g.,
already undistorted images or high-quality industrial lenses).</p></li>
<li><p><strong>OPENCV</strong>: A good choice for wider-angle lenses with moderate distortion.
Models 2 focal lengths, principal point, and 4 distortion parameters (2 radial
+ 2 tangential).</p></li>
<li><p><strong>SIMPLE_RADIAL_FISHEYE</strong> or <strong>OPENCV_FISHEYE</strong>: Use for fisheye lenses with
a field of view significantly larger than 120 degrees.</p></li>
<li><p><strong>FULL_OPENCV</strong>: Use only when you have many images sharing intrinsics
and need to model complex distortion patterns. With 12 parameters, this model
requires a large number of observations to converge reliably.</p></li>
</ul>
<p>As a rule of thumb, use the simplest model that adequately describes your lens.
Overly complex models with many parameters can lead to degenerate or overfitted
calibration, especially when few images share intrinsics. If in doubt, start
with <code class="docutils literal notranslate"><span class="pre">SIMPLE_RADIAL</span></code> and inspect the reprojection errors in the model
statistics.</p>
</section>
<section id="using-calibration-from-opencv-kalibr-or-other-tools">
<h2>Using calibration from OpenCV, Kalibr, or other tools<a class="headerlink" href="#using-calibration-from-opencv-kalibr-or-other-tools" title="Link to this heading">#</a></h2>
<p>If you already calibrated your camera with an external tool such as OpenCV or
Kalibr, you can reuse those intrinsics in COLMAP (see <a class="reference internal" href="#faq-fix-intrinsics"><span class="std std-ref">Fix intrinsics</span></a> to keep them constant during reconstruction). Two
conventions have to be matched first.</p>
<p><strong>Pixel coordinate convention.</strong> COLMAP places the origin at the top-left
<em>corner</em> of the image, so the center of the top-left pixel is at <code class="docutils literal notranslate"><span class="pre">(0.5,</span> <span class="pre">0.5)</span></code>
and a centered principal point is <code class="docutils literal notranslate"><span class="pre">(width</span> <span class="pre">/</span> <span class="pre">2,</span> <span class="pre">height</span> <span class="pre">/</span> <span class="pre">2)</span></code>. OpenCV and Kalibr
place integer coordinates at pixel <em>centers</em>, so their centered principal point
is <code class="docutils literal notranslate"><span class="pre">((width</span> <span class="pre">-</span> <span class="pre">1)</span> <span class="pre">/</span> <span class="pre">2,</span> <span class="pre">(height</span> <span class="pre">-</span> <span class="pre">1)</span> <span class="pre">/</span> <span class="pre">2)</span></code>. To convert a principal point from
OpenCV/Kalibr to COLMAP, add <code class="docutils literal notranslate"><span class="pre">0.5</span></code> to both <code class="docutils literal notranslate"><span class="pre">cx</span></code> and <code class="docutils literal notranslate"><span class="pre">cy</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">cx_colmap</span> <span class="o">=</span> <span class="n">cx_opencv</span> <span class="o">+</span> <span class="mf">0.5</span>
<span class="n">cy_colmap</span> <span class="o">=</span> <span class="n">cy_opencv</span> <span class="o">+</span> <span class="mf">0.5</span>
</pre></div>
</div>
<p>For example, an OpenCV calibration of an 800×600 image with a centered principal
point <code class="docutils literal notranslate"><span class="pre">(399.5,</span> <span class="pre">299.5)</span></code> becomes <code class="docutils literal notranslate"><span class="pre">(400.0,</span> <span class="pre">300.0)</span></code> in COLMAP. The focal lengths
<code class="docutils literal notranslate"><span class="pre">fx</span></code>, <code class="docutils literal notranslate"><span class="pre">fy</span></code> and the distortion coefficients are unaffected by this shift.</p>
<p><strong>Distortion parameter order.</strong> COLMAP’s <code class="docutils literal notranslate"><span class="pre">OPENCV</span></code> model uses the same
<code class="docutils literal notranslate"><span class="pre">k1,</span> <span class="pre">k2,</span> <span class="pre">p1,</span> <span class="pre">p2</span></code> distortion parameters as OpenCV, and <code class="docutils literal notranslate"><span class="pre">FULL_OPENCV</span></code>
additionally uses <code class="docutils literal notranslate"><span class="pre">k3,</span> <span class="pre">k4,</span> <span class="pre">k5,</span> <span class="pre">k6</span></code>, in that order (see <a class="reference internal" href="cameras.html"><span class="doc">Camera Models</span></a>). A
camera line in <code class="docutils literal notranslate"><span class="pre">cameras.txt</span></code> for the example above is therefore:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="n">OPENCV</span> <span class="mi">800</span> <span class="mi">600</span> <span class="n">fx</span> <span class="n">fy</span> <span class="mf">400.0</span> <span class="mf">300.0</span> <span class="n">k1</span> <span class="n">k2</span> <span class="n">p1</span> <span class="n">p2</span>
</pre></div>
</div>
</section>
<section id="choosing-between-incremental-global-and-hierarchical-sfm">
<h2>Choosing between incremental, global, and hierarchical SfM<a class="headerlink" href="#choosing-between-incremental-global-and-hierarchical-sfm" title="Link to this heading">#</a></h2>
<p>COLMAP offers three SfM pipelines:</p>
<ul class="simple">
<li><p><strong>Incremental mapper</strong> (<code class="docutils literal notranslate"><span class="pre">mapper</span></code>, default): Reconstructs the scene by
incrementally adding one image at a time. This is the most robust and
well-tested pipeline, but can become slow for large image collections, where
repeated bundle adjustment is often the bottleneck. This can be accelerated
substantially with the GPU-based Caspar backend (see
<a class="reference internal" href="#speedup-bundle-adjustment"><span class="std std-ref">Speedup bundle adjustment</span></a>).</p></li>
<li><p><strong>Global mapper</strong> (<code class="docutils literal notranslate"><span class="pre">global_mapper</span></code>): Solves for all camera poses
simultaneously using rotation averaging and global positioning. This can be
faster for large datasets with good matching graphs, but may be less robust
to outliers in the matching. The global mapper depends on good focal length
priors. If reliable intrinsics are not available, run
<code class="docutils literal notranslate"><span class="pre">view_graph_calibrator</span></code> before <code class="docutils literal notranslate"><span class="pre">global_mapper</span></code> to estimate them from the
view graph (optional but recommended to improve the quality of
global SfM). Note that <code class="docutils literal notranslate"><span class="pre">view_graph_calibrator</span></code> modifies the database
in-place, so it is recommended to work on a copy.</p></li>
<li><p><strong>Hierarchical mapper</strong> (<code class="docutils literal notranslate"><span class="pre">hierarchical_mapper</span></code>): Partitions the scene into
overlapping sub-models and reconstructs each independently, then merges them.
This is useful for very large-scale datasets where the incremental approach
becomes too slow but is usually less robust than the other two pipelines.</p></li>
</ul>
<p>All three can also be selected via the <code class="docutils literal notranslate"><span class="pre">automatic_reconstructor</span></code> using
<code class="docutils literal notranslate"><span class="pre">--mapper</span> <span class="pre">INCREMENTAL</span></code>, <code class="docutils literal notranslate"><span class="pre">--mapper</span> <span class="pre">GLOBAL</span></code>, or <code class="docutils literal notranslate"><span class="pre">--mapper</span> <span class="pre">HIERARCHICAL</span></code>.</p>
</section>
<section id="reconstruction-with-pose-priors-gps">
<h2>Reconstruction with pose priors (GPS)<a class="headerlink" href="#reconstruction-with-pose-priors-gps" title="Link to this heading">#</a></h2>
<p>If your images have GPS information in their EXIF metadata, COLMAP
automatically extracts and stores it as pose priors in the database during
feature extraction. These priors can then be used during reconstruction with
the <code class="docutils literal notranslate"><span class="pre">pose_prior_mapper</span></code>:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>colmap feature_extractor \
--database_path $PROJECT_PATH/database.db \
--image_path $PROJECT_PATH/images
colmap exhaustive_matcher \
--database_path $PROJECT_PATH/database.db
colmap pose_prior_mapper \
--database_path $PROJECT_PATH/database.db \
--image_path $PROJECT_PATH/images \
--output_path $PROJECT_PATH/sparse
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">pose_prior_mapper</span></code> is essentially the incremental mapper with prior
position constraints enabled. You can override the priors covariance (uncertainty)
using <code class="docutils literal notranslate"><span class="pre">--overwrite_priors_covariance</span></code>. The new covariance will be built based
on the values of <code class="docutils literal notranslate"><span class="pre">--prior_position_std_x</span></code>, <code class="docutils literal notranslate"><span class="pre">--prior_position_std_y</span></code>, and
<code class="docutils literal notranslate"><span class="pre">--prior_position_std_z</span></code> (default: 1.0 meter each).</p>
<p>For geo-registration of an already reconstructed model (without using priors
during mapping), see the <a class="reference internal" href="#geo-registration">Geo-registration</a> section.</p>
</section>
<section id="share-intrinsics">
<span id="faq-share-intrinsics"></span><h2>Share intrinsics<a class="headerlink" href="#share-intrinsics" title="Link to this heading">#</a></h2>
<p>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 <code class="docutils literal notranslate"><span class="pre">camera_id</span></code> property in the database. You can add new cameras
and set shared intrinsics in the database management tool. Please, refer to
<a class="reference internal" href="tutorial.html#database-management"><span class="std std-ref">Database Management</span></a> for more information.</p>
</section>
<section id="set-known-camera-intrinsics">
<h2>Set known camera intrinsics<a class="headerlink" href="#set-known-camera-intrinsics" title="Link to this heading">#</a></h2>
<p>If the camera calibration is known a priori, the recommended way to provide it
is during feature extraction using the <code class="docutils literal notranslate"><span class="pre">ImageReader</span></code> options:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>colmap feature_extractor \
--database_path $PROJECT_PATH/database.db \
--image_path $PROJECT_PATH/images \
--ImageReader.single_camera 1 \
--ImageReader.camera_model OPENCV \
--ImageReader.camera_params "fx,fy,cx,cy,k1,k2,p1,p2"
</pre></div>
</div>
<p>The parameters must be provided as a comma-separated list in the order defined
by the chosen camera model (see <a class="reference internal" href="cameras.html"><span class="doc">Camera Models</span></a>). In the GUI, the equivalent
settings can be found under <code class="docutils literal notranslate"><span class="pre">Processing</span> <span class="pre">></span> <span class="pre">Feature</span> <span class="pre">extraction</span> <span class="pre">></span> <span class="pre">Custom</span>
<span class="pre">parameters</span></code>. Use <code class="docutils literal notranslate"><span class="pre">--ImageReader.single_camera</span> <span class="pre">1</span></code> if all images were captured
by the same physical camera with identical settings, so that they share one
camera in the database (see <a class="reference internal" href="#share-intrinsics">Share intrinsics</a>).</p>
<p>To modify the intrinsics of an existing database, do not edit the SQLite tables
by hand (the parameters are stored as binary blobs of doubles), but use
pycolmap’s database API instead:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span><span class="w"> </span><span class="nn">pycolmap</span>
<span class="k">with</span> <span class="n">pycolmap</span><span class="o">.</span><span class="n">Database</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s2">"path/to/database.db"</span><span class="p">)</span> <span class="k">as</span> <span class="n">db</span><span class="p">:</span>
<span class="n">camera</span> <span class="o">=</span> <span class="n">db</span><span class="o">.</span><span class="n">read_camera</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span>
<span class="n">camera</span><span class="o">.</span><span class="n">params</span> <span class="o">=</span> <span class="p">[</span><span class="n">fx</span><span class="p">,</span> <span class="n">fy</span><span class="p">,</span> <span class="n">cx</span><span class="p">,</span> <span class="n">cy</span><span class="p">,</span> <span class="n">k1</span><span class="p">,</span> <span class="n">k2</span><span class="p">,</span> <span class="n">p1</span><span class="p">,</span> <span class="n">p2</span><span class="p">]</span>
<span class="n">camera</span><span class="o">.</span><span class="n">has_prior_focal_length</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">db</span><span class="o">.</span><span class="n">update_camera</span><span class="p">(</span><span class="n">camera</span><span class="p">)</span>
</pre></div>
</div>
<p>Note that the provided parameters are still refined during bundle adjustment by
default. To keep them fixed during the reconstruction, see
<a class="reference internal" href="#faq-fix-intrinsics"><span class="std std-ref">Fix intrinsics</span></a>.</p>
</section>
<section id="fix-intrinsics">
<span id="faq-fix-intrinsics"></span><h2>Fix intrinsics<a class="headerlink" href="#fix-intrinsics" title="Link to this heading">#</a></h2>
<p>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.</p>
<p>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
<code class="docutils literal notranslate"><span class="pre">Reconstruction</span> <span class="pre">></span> <span class="pre">Reconstruction</span> <span class="pre">options</span> <span class="pre">></span> <span class="pre">Bundle</span> <span class="pre">Adj.</span> <span class="pre">></span> <span class="pre">refine_*</span></code> 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 <code class="docutils literal notranslate"><span class="pre">Reconstruction</span> <span class="pre">></span> <span class="pre">Bundle</span> <span class="pre">adj.</span>
<span class="pre">options</span> <span class="pre">></span> <span class="pre">refine_*</span></code> and then running <code class="docutils literal notranslate"><span class="pre">Reconstruction</span> <span class="pre">></span> <span class="pre">Bundle</span> <span class="pre">adjustment</span></code>.</p>
</section>
<section id="principal-point-refinement">
<h2>Principal point refinement<a class="headerlink" href="#principal-point-refinement" title="Link to this heading">#</a></h2>
<p>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
<a class="reference internal" href="#faq-fix-intrinsics"><span class="std std-ref">Fix intrinsics</span></a> for more information.</p>
</section>
<section id="increase-number-of-matches-sparse-3d-points">
<h2>Increase number of matches / sparse 3D points<a class="headerlink" href="#increase-number-of-matches-sparse-3d-points" title="Link to this heading">#</a></h2>
<p>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: <code class="docutils literal notranslate"><span class="pre">--SiftExtraction.estimate_affine_shape=true</span></code> and
<code class="docutils literal notranslate"><span class="pre">--SiftExtraction.domain_size_pooling=true</span></code>. In addition, you should enable
guided feature matching using: <code class="docutils literal notranslate"><span class="pre">--FeatureMatching.guided_matching=true</span></code>.</p>
<p>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 <code class="docutils literal notranslate"><span class="pre">Reconstruction</span> <span class="pre">></span> <span class="pre">Reconstruction</span> <span class="pre">options</span> <span class="pre">></span> <span class="pre">Triangulation</span> <span class="pre">></span>
<span class="pre">ignore_two_view_tracks</span></code>. If your images are taken from far distance with
respect to the scene, you can try to reduce the minimum triangulation angle.</p>
</section>
<section id="reconstruct-sparse-dense-model-from-known-camera-poses">
<h2>Reconstruct sparse/dense model from known camera poses<a class="headerlink" href="#reconstruct-sparse-dense-model-from-known-camera-poses" title="Link to this heading">#</a></h2>
<p>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 <code class="docutils literal notranslate"><span class="pre">cameras.txt</span></code>, <code class="docutils literal notranslate"><span class="pre">points3D.txt</span></code>, and <code class="docutils literal notranslate"><span class="pre">images.txt</span></code> under a new folder:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>+── path/to/manually/created/sparse/model
│ +── cameras.txt
│ +── images.txt
│ +── points3D.txt
</pre></div>
</div>
<p>The <code class="docutils literal notranslate"><span class="pre">points3D.txt</span></code> file should be empty while every other line in the <code class="docutils literal notranslate"><span class="pre">images.txt</span></code>
should also be empty, since the sparse features are computed, as described below. You can
refer to <a class="reference internal" href="format.html#output-format"><span class="std std-ref">this article</span></a> for more information about the structure of
a sparse model.</p>
<p>Example of images.txt:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">1</span> <span class="mf">0.695104</span> <span class="mf">0.718385</span> <span class="o">-</span><span class="mf">0.024566</span> <span class="mf">0.012285</span> <span class="o">-</span><span class="mf">0.046895</span> <span class="mf">0.005253</span> <span class="o">-</span><span class="mf">0.199664</span> <span class="mi">1</span> <span class="n">image0001</span><span class="o">.</span><span class="n">png</span>
<span class="c1"># Make sure every other line is left empty</span>
<span class="mi">2</span> <span class="mf">0.696445</span> <span class="mf">0.717090</span> <span class="o">-</span><span class="mf">0.023185</span> <span class="mf">0.014441</span> <span class="o">-</span><span class="mf">0.041213</span> <span class="mf">0.001928</span> <span class="o">-</span><span class="mf">0.134851</span> <span class="mi">2</span> <span class="n">image0002</span><span class="o">.</span><span class="n">png</span>
<span class="mi">3</span> <span class="mf">0.697457</span> <span class="mf">0.715925</span> <span class="o">-</span><span class="mf">0.025383</span> <span class="mf">0.018967</span> <span class="o">-</span><span class="mf">0.054056</span> <span class="mf">0.008579</span> <span class="o">-</span><span class="mf">0.378221</span> <span class="mi">1</span> <span class="n">image0003</span><span class="o">.</span><span class="n">png</span>
<span class="mi">4</span> <span class="mf">0.698777</span> <span class="mf">0.714625</span> <span class="o">-</span><span class="mf">0.023996</span> <span class="mf">0.021129</span> <span class="o">-</span><span class="mf">0.048184</span> <span class="mf">0.004529</span> <span class="o">-</span><span class="mf">0.313427</span> <span class="mi">2</span> <span class="n">image0004</span><span class="o">.</span><span class="n">png</span>
</pre></div>
</div>
<p>Each image above must have the same <code class="docutils literal notranslate"><span class="pre">image_id</span></code> (first column) as in the database (next step).
This database can be inspected either in the GUI (under <code class="docutils literal notranslate"><span class="pre">Database</span> <span class="pre">management</span> <span class="pre">></span> <span class="pre">Processing</span></code>),
or, one can create a reconstruction with colmap and later export it as text in order to see
the images.txt file it creates.</p>
<p>To reconstruct a sparse map, you first have to recompute features from the
images of the known camera poses as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>colmap feature_extractor \
--database_path $PROJECT_PATH/database.db \
--image_path $PROJECT_PATH/images
</pre></div>
</div>
<p>If your known camera intrinsics have large distortion coefficients, you should
now manually copy the parameters from your <code class="docutils literal notranslate"><span class="pre">cameras.txt</span></code> 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 pycolmap’s database API.
Otherwise, you can skip this step and
simply continue as follows:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>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
</pre></div>
</div>
<p>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:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>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
</pre></div>
</div>
<p>Alternatively, you can also produce a dense model without a sparse model as:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>colmap image_undistorter \
--image_path $PROJECT_PATH/images \
--input_path path/to/manually/created/sparse/model \
--output_path path/to/dense/workspace
</pre></div>
</div>
<p>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 <a class="reference internal" href="#faq-dense-manual-source"><span class="std std-ref">here</span></a>. The dense stereo stage
now also requires a manual specification of the depth range.</p>