Update (#11)

Author: sarlinpe

.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 7ecbe026126241ce4cdf145a49680b0e
+config: ee05e8cf3f65935f592597f6844d6280
 tags: 645f666f9bcd5a90fca523b33c5a78b7

_sources/cameras.rst.txt

@@ -5,24 +5,24 @@ COLMAP implements different camera models of varying complexity. If no intrinsic
 parameters are known a priori, it is generally best to use the simplest camera
 model that is complex enough to model the distortion effects:
 
-- ``SIMPLE_PINHOLE``, ``PINHOLE``: Use these camera models,
-  if your images are undistorted a priori. These use one and two focal length
-  parameters, respectively. Note that even in the case of undistorted images, 
-  COLMAP could try to improve the intrinsics with a more complex camera model.
-- ``SIMPLE_RADIAL``, ``RADIAL``: This should be the camera model of choice,
-  if the intrinsics are unknown and every image has a different camera
-  calibration, e.g., in the case of Internet photos. Both models are simplified
-  versions of the ``OPENCV`` model only modeling radial distortion
-  effects with one and two parameters, respectively.
-- ``OPENCV``, ``FULL_OPENCV``: Use these camera models, if
-  you know the calibration parameters a priori. You can also try to let COLMAP
-  estimate the parameters, if you share the intrinsics for multiple images. Note
-  that the automatic estimation of parameters will most likely fail, if every
-  image has a separate set of intrinsic parameters.
-- ``SIMPLE_RADIAL_FISHEYE``, ``RADIAL_FISHEYE``, ``OPENCV_FISHEYE``, ``FOV``,
-  ``THIN_PRISM_FISHEYE``: Use these camera models for fisheye lenses
-  and note that all other models are not really capable of modeling the
-  distortion effects of fisheye lenses. The ``FOV`` model is used by
+- `SIMPLE_PINHOLE`, `PINHOLE`: Use these camera models, if your images are
+  undistorted a priori. These use one and two focal length parameters,
+  respectively. Note that even in the case of undistorted images, COLMAP could
+  try to improve the intrinsics with a more complex camera model.
+- `SIMPLE_RADIAL`, `RADIAL`: This should be the camera model of choice, if the
+  intrinsics are unknown and every image has a different camera calibration,
+  e.g., in the case of Internet photos. Both models are simplified versions of
+  the `OPENCV` model only modeling radial distortion effects with one and two
+  parameters, respectively.
+- `OPENCV`, `FULL_OPENCV`: Use these camera models, if you know the calibration
+  parameters a priori. You can also try to let COLMAP estimate the parameters,
+  if you share the intrinsics for multiple images. Note that the automatic
+  estimation of parameters will most likely fail, if every image has a separate
+  set of intrinsic parameters.
+- `SIMPLE_RADIAL_FISHEYE`, `RADIAL_FISHEYE`, `OPENCV_FISHEYE`, `FOV`,
+  `THIN_PRISM_FISHEYE`, `RAD_TAN_THIN_PRISM_FISHEYE`: Use these camera models
+  for fisheye lenses and note that all other models are not really capable of
+  modeling the distortion effects of fisheye lenses. The `FOV` model is used by
   Google Project Tango (make sure to not initialize `omega` to zero).
 
 You can inspect the estimated intrinsic parameters by double-clicking specific

_sources/changelog.rst.txt

@@ -1,6 +1,3 @@
 .. _changelog:
 
-Changelog
-=========
-
-.. include:: ../CHANGELOG.txt
+.. include:: ../CHANGELOG.rst

_sources/index.rst.txt

@@ -82,11 +82,21 @@ for bug reports, feature requests/additions, etc.
 Acknowledgments
 ---------------
 
-The library was originally written by `Johannes L. Schönberger
-<https://demuc.de/>`_ with funding provided by his PhD advisors Jan-Michael
-Frahm and Marc Pollefeys. Since then the project has benefited from countless
-community contributions, including bug fixes, improvements, new features,
-third-party tooling, and community support.
+COLMAP was originally written by `Johannes Schönberger <https://demuc.de/>`__ with
+funding provided by his PhD advisors Jan-Michael Frahm and Marc Pollefeys.
+The team of core project maintainers currently includes
+`Johannes Schönberger <https://github.com/ahojnnes>`__,
+`Paul-Edouard Sarlin <https://github.com/sarlinpe>`_, and
+`Shaohui Liu <https://github.com/B1ueber2y>`_.
+
+The Python bindings in PyCOLMAP were originally added by
+`Mihai Dusmanu <https://github.com/mihaidusmanu>`_,
+`Philipp Lindenberger <https://github.com/Phil26AT>`_, and
+`Paul-Edouard Sarlin <https://github.com/sarlinpe>`_.
+
+The project has also benefitted from countless community contributions, including
+bug fixes, improvements, new features, third-party tooling, and community
+support (special credits to `Torsten Sattler <https://tsattler.github.io>`_).
 
 
 .. toctree::

_sources/install.rst.txt

@@ -5,10 +5,16 @@ Installation
 
 You can either download one of the pre-built binaries or build the source code
 manually. Pre-built binaries and other resources can be downloaded from
-https://demuc.de/colmap/. An overview of system packages for Linux/Unix/BSD
-distributions are available at https://repology.org/metapackage/colmap/versions.
-Note that the COLMAP packages in the default repositories for Linux/Unix/BSD do
-not come with CUDA support, which requires a manual build from source.
+https://demuc.de/colmap/.
+
+An overview of system packages for Linux/Unix/BSD distributions are available at
+https://repology.org/metapackage/colmap/versions. Note that the COLMAP packages
+in the default repositories for Linux/Unix/BSD do not come with CUDA support,
+which requires a manual build from source, as explained further below.
+
+For Mac users, `Homebrew <https://brew.sh>`__ provides a formula for COLMAP with
+pre-compiled binaries or the option to build from source. After installing
+homebrew, installing COLMAP is as easy as running `brew install colmap`.
 
 COLMAP can be used as an independent application through the command-line or
 graphical user interface. Alternatively, COLMAP is also built as a reusable
@@ -35,18 +41,6 @@ are not officially signed. The provided COLMAP binaries are automatically built
 from GitHub Actions CI machines. If you do not trust them, you can build from
 source as described below.
 
-Mac
----
-
-The pre-built application package for Mac contains both the GUI and command-line
-version of COLMAP. To open the GUI, simply open the application. Note that
-COLMAP is shipped as an unsigned application, i.e., when your first open the
-application, you have to right-click the application and select *Open* and then
-accept to trust the application. In the future, you can then simply double-click
-the application to open COLMAP. The command-line interface is accessible by
-running the packaged binary ``COLMAP.app/Contents/MacOS/colmap``. To list the
-available COLMAP commands, run ``COLMAP.app/Contents/MacOS/colmap -h``.
-
 
 -----------------
 Build from Source
@@ -78,7 +72,6 @@ Dependencies from the default Ubuntu repositories::
         ninja-build \
         build-essential \
         libboost-program-options-dev \
-        libboost-filesystem-dev \
         libboost-graph-dev \
         libboost-system-dev \
         libeigen3-dev \
@@ -138,7 +131,7 @@ CUDA package and GCC, and you must compile against GCC 10::
 Mac
 ---
 
-Dependencies from `Homebrew <http://brew.sh/>`_::
+Dependencies from `Homebrew <http://brew.sh/>`__::
 
     brew install \
         cmake \

_sources/tutorial.rst.txt

@@ -277,7 +277,7 @@ 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
+Please, choose ``Processing > Feature matching`` 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

_static/documentation_options.js

@@ -1,5 +1,5 @@
 const DOCUMENTATION_OPTIONS = {
-    VERSION: '3.11.0.dev0',
+    VERSION: '3.12.0.dev0',
     LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',

bibliography.html

@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Bibliography &mdash; COLMAP 3.11.0.dev0 documentation</title>
+  <title>Bibliography &mdash; COLMAP 3.12.0.dev0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/custom.css?v=4eec7147" />
@@ -16,7 +16,7 @@
 
         <script src="_static/jquery.js?v=5d32c60e"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-        <script src="_static/documentation_options.js?v=bf522350"></script>
+        <script src="_static/documentation_options.js?v=3a07dcc1"></script>
         <script src="_static/doctools.js?v=9a2dae69"></script>
         <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/js/theme.js"></script>
@@ -37,7 +37,7 @@
             COLMAP
           </a>
               <div class="version">
-                3.11.0.dev0
+                3.12.0.dev0
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">

cameras.html

@@ -4,7 +4,7 @@
   <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
 
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Camera Models &mdash; COLMAP 3.11.0.dev0 documentation</title>
+  <title>Camera Models &mdash; COLMAP 3.12.0.dev0 documentation</title>
       <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
       <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
       <link rel="stylesheet" type="text/css" href="_static/custom.css?v=4eec7147" />
@@ -16,7 +16,7 @@
 
         <script src="_static/jquery.js?v=5d32c60e"></script>
         <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
-        <script src="_static/documentation_options.js?v=bf522350"></script>
+        <script src="_static/documentation_options.js?v=3a07dcc1"></script>
         <script src="_static/doctools.js?v=9a2dae69"></script>
         <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/js/theme.js"></script>
@@ -38,7 +38,7 @@
             COLMAP
           </a>
               <div class="version">
-                3.11.0.dev0
+                3.12.0.dev0
               </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
@@ -95,24 +95,24 @@ <h1>Camera Models<a class="headerlink" href="#camera-models" title="Link to this
 parameters are known a priori, it is generally best to use the simplest camera
 model that is complex enough to model the distortion effects:</p>
 <ul class="simple">
-<li><p><code class="docutils literal notranslate"><span class="pre">SIMPLE_PINHOLE</span></code>, <code class="docutils literal notranslate"><span class="pre">PINHOLE</span></code>: Use these camera models,
-if your images are undistorted a priori. These use one and two focal length
-parameters, respectively. Note that even in the case of undistorted images,
-COLMAP could try to improve the intrinsics with a more complex camera model.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">SIMPLE_RADIAL</span></code>, <code class="docutils literal notranslate"><span class="pre">RADIAL</span></code>: This should be the camera model of choice,
-if the intrinsics are unknown and every image has a different camera
-calibration, e.g., in the case of Internet photos. Both models are simplified
-versions of the <code class="docutils literal notranslate"><span class="pre">OPENCV</span></code> model only modeling radial distortion
-effects with one and two parameters, respectively.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">OPENCV</span></code>, <code class="docutils literal notranslate"><span class="pre">FULL_OPENCV</span></code>: Use these camera models, if
-you know the calibration parameters a priori. You can also try to let COLMAP
-estimate the parameters, if you share the intrinsics for multiple images. Note
-that the automatic estimation of parameters will most likely fail, if every
-image has a separate set of intrinsic parameters.</p></li>
-<li><p><code class="docutils literal notranslate"><span class="pre">SIMPLE_RADIAL_FISHEYE</span></code>, <code class="docutils literal notranslate"><span class="pre">RADIAL_FISHEYE</span></code>, <code class="docutils literal notranslate"><span class="pre">OPENCV_FISHEYE</span></code>, <code class="docutils literal notranslate"><span class="pre">FOV</span></code>,
-<code class="docutils literal notranslate"><span class="pre">THIN_PRISM_FISHEYE</span></code>: Use these camera models for fisheye lenses
-and note that all other models are not really capable of modeling the
-distortion effects of fisheye lenses. The <code class="docutils literal notranslate"><span class="pre">FOV</span></code> model is used by
+<li><p><cite>SIMPLE_PINHOLE</cite>, <cite>PINHOLE</cite>: Use these camera models, if your images are
+undistorted a priori. These use one and two focal length parameters,
+respectively. Note that even in the case of undistorted images, COLMAP could
+try to improve the intrinsics with a more complex camera model.</p></li>
+<li><p><cite>SIMPLE_RADIAL</cite>, <cite>RADIAL</cite>: This should be the camera model of choice, if the
+intrinsics are unknown and every image has a different camera calibration,
+e.g., in the case of Internet photos. Both models are simplified versions of
+the <cite>OPENCV</cite> model only modeling radial distortion effects with one and two
+parameters, respectively.</p></li>
+<li><p><cite>OPENCV</cite>, <cite>FULL_OPENCV</cite>: Use these camera models, if you know the calibration
+parameters a priori. You can also try to let COLMAP estimate the parameters,
+if you share the intrinsics for multiple images. Note that the automatic
+estimation of parameters will most likely fail, if every image has a separate
+set of intrinsic parameters.</p></li>
+<li><p><cite>SIMPLE_RADIAL_FISHEYE</cite>, <cite>RADIAL_FISHEYE</cite>, <cite>OPENCV_FISHEYE</cite>, <cite>FOV</cite>,
+<cite>THIN_PRISM_FISHEYE</cite>, <cite>RAD_TAN_THIN_PRISM_FISHEYE</cite>: Use these camera models
+for fisheye lenses and note that all other models are not really capable of
+modeling the distortion effects of fisheye lenses. The <cite>FOV</cite> model is used by
 Google Project Tango (make sure to not initialize <cite>omega</cite> to zero).</p></li>
 </ul>
 <p>You can inspect the estimated intrinsic parameters by double-clicking specific