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<!DOCTYPE html>
<html lang="en" data-content_root="./" data-theme="auto">
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<meta property="og:title" content="Tutorial" />
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<meta property="og:url" content="https://colmap.github.io/tutorial.html" />
<meta property="og:site_name" content="COLMAP" />
<meta property="og:description" content="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 ..." />
<meta property="og:image" content="https://colmap.github.io/_static/og-image.png" />
<meta property="og:image:alt" content="COLMAP — Structure-from-Motion & Multi-View Stereo" />
<meta name="description" content="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 ..." />
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<title>Tutorial — COLMAP</title>
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<section id="tutorial">
<span id="id1"></span><h1>Tutorial<a class="headerlink" href="#tutorial" title="Link to this heading">#</a></h1>
<p>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 <a class="reference external" href="https://demuc.de/tutorials/cvpr2017/">CVPR 2017 Tutorial on Large-scale 3D
Modeling from Crowdsourced Data</a> and
<a class="reference internal" href="bibliography.html#schoenberger-thesis" id="id2"><span>[schoenberger_thesis]</span></a>.</p>
<p>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.</p>
<nav class="contents local" id="contents">
<p class="topic-title">Contents</p>
<ul class="simple">
<li><p><a class="reference internal" href="#quickstart" id="id23">Quickstart</a></p></li>
<li><p><a class="reference internal" href="#preface" id="id24">Preface</a></p></li>
<li><p><a class="reference internal" href="#structure-from-motion" id="id25">Structure-from-Motion</a></p></li>
<li><p><a class="reference internal" href="#multi-view-stereo" id="id26">Multi-View Stereo</a></p></li>
<li><p><a class="reference internal" href="#terminology" id="id27">Terminology</a></p></li>
<li><p><a class="reference internal" href="#data-structure" id="id28">Data Structure</a></p></li>
<li><p><a class="reference internal" href="#feature-detection-and-extraction" id="id29">Feature Detection and Extraction</a></p></li>
<li><p><a class="reference internal" href="#feature-matching-and-geometric-verification" id="id30">Feature Matching and Geometric Verification</a></p></li>
<li><p><a class="reference internal" href="#sparse-reconstruction" id="id31">Sparse Reconstruction</a></p></li>
<li><p><a class="reference internal" href="#importing-and-exporting" id="id32">Importing and Exporting</a></p></li>
<li><p><a class="reference internal" href="#dense-reconstruction" id="id33">Dense Reconstruction</a></p></li>
<li><p><a class="reference internal" href="#database-management" id="id34">Database Management</a></p></li>
<li><p><a class="reference internal" href="#graphical-and-command-line-interface" id="id35">Graphical and Command-line Interface</a></p></li>
</ul>
</nav>
<section id="quickstart">
<span id="quick-start"></span><h2><a class="toc-backref" href="#id23" role="doc-backlink">Quickstart</a><a class="headerlink" href="#quickstart" title="Link to this heading">#</a></h2>
<p>First, start the graphical user interface of COLMAP, as described <a class="reference internal" href="gui.html#gui"><span class="std std-ref">here</span></a>. 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 <code class="docutils literal notranslate"><span class="pre">Reconstruction</span> <span class="pre">></span> <span class="pre">Automatic</span> <span class="pre">Reconstruction</span></code> in the GUI
and specify the relevant options. The output is written to the workspace folder.
For example, if your images are located in <code class="docutils literal notranslate"><span class="pre">path/to/project/images</span></code>, you could
select <code class="docutils literal notranslate"><span class="pre">path/to/project</span></code> as a workspace folder and after running the automatic
reconstruction tool, the folder would look similar to this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>+── images
│ +── image1.jpg
│ +── image2.jpg
│ +── ...
+── sparse
│ +── 0
│ │ +── rigs.bin
│ │ +── cameras.bin
│ │ +── frames.bin
│ │ +── images.bin
│ │ +── points3D.bin
│ +── ...
+── dense
│ +── 0
│ │ +── images
│ │ +── sparse
│ │ +── stereo
│ │ +── fused.ply
│ │ +── meshed-poisson.ply
│ │ +── meshed-delaunay.ply
│ │ +── meshed-advancing-front.ply
│ +── ...
+── database.db
</pre></div>
</div>
<p>Here, the <code class="docutils literal notranslate"><span class="pre">path/to/project/sparse</span></code> contains the sparse models for all
reconstructed components, while <code class="docutils literal notranslate"><span class="pre">path/to/project/dense</span></code> contains their
corresponding dense models. The dense point cloud <code class="docutils literal notranslate"><span class="pre">fused.ply</span></code> can be imported
in COLMAP using <code class="docutils literal notranslate"><span class="pre">File</span> <span class="pre">></span> <span class="pre">Import</span> <span class="pre">from</span> <span class="pre">...</span></code>, while the dense mesh must be
visualized with an external viewer such as Meshlab.</p>
<p>The same automatic reconstruction can be run from the command-line, without the
GUI, using the <code class="docutils literal notranslate"><span class="pre">automatic_reconstructor</span></code> command, which produces the identical
workspace layout shown above:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">colmap</span> <span class="n">automatic_reconstructor</span> \\
<span class="o">--</span><span class="n">workspace_path</span> <span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">project</span> \\
<span class="o">--</span><span class="n">image_path</span> <span class="n">path</span><span class="o">/</span><span class="n">to</span><span class="o">/</span><span class="n">project</span><span class="o">/</span><span class="n">images</span>
</pre></div>
</div>
<p>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.</p>
</section>
<section id="preface">
<h2><a class="toc-backref" href="#id24" role="doc-backlink">Preface</a><a class="headerlink" href="#preface" title="Link to this heading">#</a></h2>
<p>COLMAP requires only a few steps to perform 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 <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> <span class="pre">data</span></code>. If in doubt what settings to choose, stick to the
defaults. The source code contains more documentation about all parameters.</p>
<p>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
stderr. 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
also define various levels of logging verbosity.</p>
</section>
<section id="structure-from-motion">
<h2><a class="toc-backref" href="#id25" role="doc-backlink">Structure-from-Motion</a><a class="headerlink" href="#structure-from-motion" title="Link to this heading">#</a></h2>
<figure class="align-center align-default" id="id22">
<img alt="Incremental Structure-from-Motion pipeline" src="_images/incremental-sfm.webp" />
<figcaption>
<p><span class="caption-text">COLMAP’s incremental Structure-from-Motion pipeline.</span><a class="headerlink" href="#id22" title="Link to this image">#</a></p>
</figcaption>
</figure>
<p>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:</p>
<ol class="arabic simple">
<li><p>Feature detection and extraction</p></li>
<li><p>Feature matching and geometric verification</p></li>
<li><p>Structure and motion reconstruction</p></li>
</ol>
<p>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 <a class="reference internal" href="bibliography.html#schoenberger16sfm" id="id3"><span>[schoenberger16sfm]</span></a> and
<a class="reference internal" href="bibliography.html#schoenberger16mvs" id="id4"><span>[schoenberger16mvs]</span></a>.</p>
<p>If you have control over the picture capture process, please follow these
guidelines for optimal reconstruction results:</p>
<ul class="simple">
<li><p>Capture images with <strong>good texture</strong>. 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.</p></li>
<li><p>Capture images at <strong>similar illumination</strong> conditions. Avoid high dynamic
range scenes (e.g., pictures against the sun with shadows or pictures
through doors/windows). Avoid specularities on shiny surfaces.</p></li>
<li><p>Capture images with <strong>high visual overlap</strong>. Make sure that each object is
seen in at least 3 images – the more images the better.</p></li>
<li><p>Capture images from <strong>different viewpoints</strong>. 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 are not necessarily better and might lead to a
slow reconstruction process. If you use a video as input, consider
down-sampling the frame rate.</p></li>
</ul>
</section>
<section id="multi-view-stereo">
<h2><a class="toc-backref" href="#id26" role="doc-backlink">Multi-View Stereo</a><a class="headerlink" href="#multi-view-stereo" title="Link to this heading">#</a></h2>
<p>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 <a class="reference internal" href="bibliography.html#kazhdan2013" id="id5"><span>[kazhdan2013]</span></a> or the advancing front
surface reconstruction <a class="reference internal" href="bibliography.html#cohen-steiner2004" id="id6"><span>[cohen-steiner2004]</span></a> can then recover the 3D surface
geometry of the scene. The resulting meshes can optionally be simplified
using Quadric Error Metric (QEM) decimation <a class="reference internal" href="bibliography.html#garland1997" id="id7"><span>[garland1997]</span></a> to reduce their
complexity while preserving shape and appearance. Additionally, the meshes can
be textured using multi-view texture mapping <a class="reference internal" href="bibliography.html#waechter2014" id="id8"><span>[waechter2014]</span></a>, which assigns each
face to the best-view camera image and produces a texture atlas with UV
coordinates. More information on Multi-View Stereo in general and the algorithms
in COLMAP can be found in <a class="reference internal" href="bibliography.html#schoenberger16mvs" id="id9"><span>[schoenberger16mvs]</span></a>.</p>
</section>
<section id="terminology">
<h2><a class="toc-backref" href="#id27" role="doc-backlink">Terminology</a><a class="headerlink" href="#terminology" title="Link to this heading">#</a></h2>
<p>The term <strong>camera</strong> refers to a physical 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 <strong>image</strong> is
associated with a bitmap file, e.g., a JPEG or PNG file on disk. COLMAP detects
<strong>keypoints</strong> in each image whose appearance is described by numerical
<strong>descriptors</strong>. Pure appearance-based correspondences between
keypoints/descriptors are defined by <strong>matches</strong>, while <strong>inlier matches</strong> are
geometrically verified and used for the reconstruction procedure.</p>
<p>The term <strong>rig</strong> describes a fixed assembly of one or more cameras whose relative
poses are constant over time, e.g., a stereo or multi-camera setup, or a single
moving camera (a trivial rig with one camera). The term <strong>frame</strong> denotes a
single snapshot in time, i.e., the set of images captured simultaneously by all
cameras of a rig. A frame therefore groups images that share the same rig pose,
and COLMAP optimizes one pose per frame rather than one pose per image. Rigs and
frames are stored as <code class="docutils literal notranslate"><span class="pre">rigs.bin</span></code> and <code class="docutils literal notranslate"><span class="pre">frames.bin</span></code> in the reconstruction output
(see <a class="reference internal" href="rigs.html#rig-support"><span class="std std-ref">Rig Support</span></a> and <a class="reference internal" href="format.html#output-format"><span class="std std-ref">Output Format</span></a>).</p>
</section>
<section id="data-structure">
<h2><a class="toc-backref" href="#id28" role="doc-backlink">Data Structure</a><a class="headerlink" href="#data-structure" title="Link to this heading">#</a></h2>
<p>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 image formats through OpenImageIO. 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 <a class="reference internal" href="database.html"><span class="doc">Database Format</span></a>).</p>
<p>The first step is to start the graphical user interface of COLMAP by running the
pre-built binaries (Windows: <code class="docutils literal notranslate"><span class="pre">COLMAP.bat</span></code>, Mac: <code class="docutils literal notranslate"><span class="pre">COLMAP.app</span></code>) or by executing
<code class="docutils literal notranslate"><span class="pre">./src/colmap/exe/colmap</span> <span class="pre">gui</span></code> from the CMake build folder. Next, create a new project
by choosing <code class="docutils literal notranslate"><span class="pre">File</span> <span class="pre">></span> <span class="pre">New</span> <span class="pre">project</span></code>. 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 <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>. 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 <code class="docutils literal notranslate"><span class="pre">.ini</span></code> 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 <code class="docutils literal notranslate"><span class="pre">File</span> <span class="pre">></span> <span class="pre">Open</span> <span class="pre">project</span></code> 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
<a class="reference internal" href="#interface"><span class="std std-ref">Interface</span></a>).</p>
<p>An example folder structure could look like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span>/path/to/project/...
+── images
│ +── image1.jpg
│ +── image2.jpg
│ +── ...
│ +── imageN.jpg
+── database.db
+── project.ini
</pre></div>
</div>
<p>In this example, you would select <code class="docutils literal notranslate"><span class="pre">/path/to/project/images</span></code> as the image folder
path, <code class="docutils literal notranslate"><span class="pre">/path/to/project/database.db</span></code> as the database file path, and save the
project configuration to <code class="docutils literal notranslate"><span class="pre">/path/to/project/project.ini</span></code>.</p>
</section>
<section id="feature-detection-and-extraction">
<h2><a class="toc-backref" href="#id29" role="doc-backlink">Feature Detection and Extraction</a><a class="headerlink" href="#feature-detection-and-extraction" title="Link to this heading">#</a></h2>
<p>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 a single step, so
that each image is loaded from disk only once.</p>
<p>Next, choose <code class="docutils literal notranslate"><span class="pre">Processing</span> <span class="pre">></span> <span class="pre">Extract</span> <span class="pre">features</span></code>. In this dialog, you must first
decide on the intrinsic camera model to use. 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 <a class="reference internal" href="#database-management"><span class="std std-ref">Database Management</span></a>). If in doubt what to choose in this step, simply stick
to the default parameters.</p>
<p>You can either detect and extract new features from the images or import
existing features from text files. By default, COLMAP extracts SIFT <a class="reference internal" href="bibliography.html#lowe04" id="id10"><span>[lowe04]</span></a>
features either on the GPU or the CPU. When COLMAP is built with CUDA support
(recommended), GPU feature extraction runs without an attached display and is
suitable for headless servers. The OpenGL-based fallback (used when CUDA is not
available) instead requires an attached display, so on such systems 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 for high-contrast images.
COLMAP also supports ALIKED and LoMa feature extraction, learned feature
extractors using ONNX models, which can be selected via the
<code class="docutils literal notranslate"><span class="pre">--FeatureExtraction.type</span></code> option (see
<a class="reference internal" href="features.html#features"><span class="std std-ref">Feature Extraction and Matching</span></a> for details). If
you import existing features, every image must have a text file next to it (e.g.,
<code class="docutils literal notranslate"><span class="pre">/path/to/image1.jpg</span></code> and <code class="docutils literal notranslate"><span class="pre">/path/to/image1.jpg.txt</span></code>) in the following format:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">NUM_FEATURES</span> <span class="mi">128</span>
<span class="n">X</span> <span class="n">Y</span> <span class="n">SCALE</span> <span class="n">ORIENTATION</span> <span class="n">D_1</span> <span class="n">D_2</span> <span class="n">D_3</span> <span class="o">...</span> <span class="n">D_128</span>
<span class="o">...</span>
<span class="n">X</span> <span class="n">Y</span> <span class="n">SCALE</span> <span class="n">ORIENTATION</span> <span class="n">D_1</span> <span class="n">D_2</span> <span class="n">D_3</span> <span class="o">...</span> <span class="n">D_128</span>
</pre></div>
</div>
<p>where <code class="docutils literal notranslate"><span class="pre">X,</span> <span class="pre">Y,</span> <span class="pre">SCALE,</span> <span class="pre">ORIENTATION</span></code> are floating point numbers and <code class="docutils literal notranslate"><span class="pre">D_1...D_128</span></code>
values in the range <code class="docutils literal notranslate"><span class="pre">0...255</span></code>. The file should have <code class="docutils literal notranslate"><span class="pre">NUM_FEATURES</span></code> lines with
one line per feature. For example, if an image has 4 features, then the text
file should look something like this:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="mi">4</span> <span class="mi">128</span>
<span class="mf">1.2</span> <span class="mf">2.3</span> <span class="mf">0.1</span> <span class="mf">0.3</span> <span class="mi">1</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">4</span> <span class="o">...</span> <span class="mi">21</span>
<span class="mf">2.2</span> <span class="mf">3.3</span> <span class="mf">1.1</span> <span class="mf">0.3</span> <span class="mi">3</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">2</span> <span class="o">...</span> <span class="mi">32</span>
<span class="mf">0.2</span> <span class="mf">1.3</span> <span class="mf">1.1</span> <span class="mf">0.3</span> <span class="mi">3</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">2</span> <span class="o">...</span> <span class="mi">2</span>
<span class="mf">1.2</span> <span class="mf">2.3</span> <span class="mf">1.1</span> <span class="mf">0.3</span> <span class="mi">3</span> <span class="mi">2</span> <span class="mi">3</span> <span class="mi">2</span> <span class="o">...</span> <span class="mi">3</span>
</pre></div>
</div>
<p>Note that by convention the upper left corner of an image has coordinate
<code class="docutils literal notranslate"><span class="pre">(0,</span> <span class="pre">0)</span></code> and the center of the upper left most pixel has coordinate
<code class="docutils literal notranslate"><span class="pre">(0.5,</span> <span class="pre">0.5)</span></code>. 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
<a class="reference internal" href="database.html#database-format"><span class="std std-ref">Database Format</span></a>).</p>
<p>If you are done setting all options, choose <code class="docutils literal notranslate"><span class="pre">Extract</span></code> 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.</p>
<p>All extracted data will be stored in the database file and can be
reviewed/managed in the database management tool (see <a class="reference internal" href="#database-management"><span class="std std-ref">Database Management</span></a>) or, for experts, directly modified using SQLite (see
<a class="reference internal" href="database.html#database-format"><span class="std std-ref">Database Format</span></a>).</p>
</section>
<section id="feature-matching-and-geometric-verification">
<h2><a class="toc-backref" href="#id30" role="doc-backlink">Feature Matching and Geometric Verification</a><a class="headerlink" href="#feature-matching-and-geometric-verification" title="Link to this heading">#</a></h2>
<p>In the second step, feature matching and geometric verification finds
correspondences between the feature points in different images.</p>
<p>Please choose <code class="docutils literal notranslate"><span class="pre">Processing</span> <span class="pre">></span> <span class="pre">Feature</span> <span class="pre">matching</span></code> and select one of the provided
matching modes, which are intended for different input scenarios:</p>
<ul>
<li><p><strong>Exhaustive Matching</strong>: If the number of images in your dataset is
relatively low (up to several hundreds), this matching mode should be fast
enough and lead 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.</p></li>
<li><p><strong>Sequential Matching</strong>: 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 (<code class="docutils literal notranslate"><span class="pre">--SequentialMatching.loop_detection_period</span></code>)
is matched against its visually most similar images
(<code class="docutils literal notranslate"><span class="pre">--SequentialMatching.loop_detection_num_images</span></code>). Retrieved images can
be restricted to those that are sufficiently far from the query in the
sequence (<code class="docutils literal notranslate"><span class="pre">--SequentialMatching.loop_detection_min_index_distance</span></code>), so
that nearby images do not consume the loop detection budget. A value of zero
disables this restriction. Note that image file names must be ordered
sequentially (e.g., <code class="docutils literal notranslate"><span class="pre">image0001.jpg</span></code>,
<code class="docutils literal notranslate"><span class="pre">image0002.jpg</span></code>, 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. A default tree will be
automatically downloaded and cached. More trees are available and can be