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Adding bibtex
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julianrendell authored May 28, 2020
2 parents 3b41db3 + 4b84d1e commit e6b137b
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2 changes: 1 addition & 1 deletion .travis.yml
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Expand Up @@ -6,7 +6,7 @@ python:
- 3.5

install:
- pip install sphinx sphinx_rtd_theme
- pip install -r requirements.txt
- source ./ci/texlive/texlive_install.sh

cache:
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12 changes: 8 additions & 4 deletions README.md
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Expand Up @@ -24,17 +24,19 @@ We could use some help with:
- checking grammar and spelling, fixing typos
- anything listed here https://github.com/alicevision/meshroom-manual/projects

# How to build on windows
# How to Build

- install Python 3
- install a latex package
- install pip and the rtd theme
```bash
pip install sphinx sphinx_rtd_theme
pip install -r requirements.txt
```

- in your cli, navigate to the `meshroom_doc directory`
- in your cli, navigate to the `meshroom-manual` directory
- type `make html` (this will re-/build the html doc)

For other build methods read the sphinx documentation
For other build methods read the sphinx documentation.

# FAQ

Expand All @@ -45,6 +47,8 @@ For other build methods read the sphinx documentation
**How to edit and preview Restructured Text?**

- You can use [Atom](https://atom.io/) with the [RestructuredText Preview Pandoc](https://atom.io/packages/rst-preview-pandoc) Plugin
- [Typora](https://www.typora.io) is a friendly editor
- [Visual Studio Code](https://code.visualstudio.com) with the [reStructuredText](https://marketplace.visualstudio.com/items?itemName=lextudio.restructuredtext) extension

**Release versions**

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3 changes: 3 additions & 0 deletions requirements.txt
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@@ -1 +1,4 @@
sphinx>=1.9.0
sphinx_rtd_theme
sphinxcontrib-bibtex

53 changes: 50 additions & 3 deletions source/conf.py
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Expand Up @@ -26,7 +26,7 @@
master_doc = 'index'

project = 'Meshroom'
copyright = '2019, Meshroom Contributors'
copyright = '2020. This work is licensed under a CC-BY-SA 4.0 International license.'
author = 'Meshroom Contributors'

# The full version, including alpha/beta/rc tags
Expand All @@ -38,7 +38,7 @@
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosectionlabel', 'sphinx_rtd_theme']
extensions = ['sphinx.ext.autodoc', 'sphinx.ext.autosectionlabel', 'sphinx_rtd_theme', 'sphinxcontrib.bibtex']
sphinxVersion = sphinx.version_info
if(sphinxVersion[0] <= 1 and sphinxVersion[1] <= 8) :
extensions.append('sphinx.ext.pngmath')
Expand Down Expand Up @@ -66,8 +66,55 @@
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
#html_static_path = ['_static']
html_static_path = []

# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
# html_logo = "mr_logo.png"

# Latex/PDF generation Config
# Based on hints from:
# - https://stackoverflow.com/questions/54147210/how-to-add-copyright-notice-to-sphinx-generated-latex-documentation
# - https://www.sphinx-doc.org/en/master/latex.html
# - https://stackoverflow.com/questions/9745854/sphinx-pdf-themes
# - https://digitalsuperpowers.com/blog/2019-02-16-publishing-ebook.html
# - https://github.com/sphinx-doc/sphinx/pull/5850

latex_contents = r'''
\setupHeadFootForFrontMatter
\formattoc
\maketitle
\signaturepage
\revisionhistory
\tableofcontents
\clearpage
\listoffigures
\clearpage
\listoftables
\clearpage
\setupHeadFootForText
\pagenumbering{arabic}
\pagestyle{plain}
'''


latex_elements = {
'fontpkg': r'''
\usepackage{lmodern}
''',
'preamble': r'''
\newcommand\sphinxbackoftitlepage{%
\vspace*{\fill}
\begin{center}
''' + u"\u00A9 " + copyright + r''' \\
Revision: ''' + release + r''' \\
\url{https://alicevision.org}
\end{center}
\vspace*{\fill}
}
'''
}


latex_show_urls = 'footnote'
6 changes: 6 additions & 0 deletions source/copyright.rst
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Copyright
=========

Copyright 2020 Meshroom Contributors.

Licensed under the Attribution-ShareAlike 4.0 International `CC BY-SA 4.0 <https://creativecommons.org/licenses/by-sa/4.0/>`_.
4 changes: 3 additions & 1 deletion source/index.rst
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Expand Up @@ -57,7 +57,7 @@ AliceVision is a Photogrammetric Computer Vision Framework which provides 3D Rec
:hidden:
:caption: References

references/references
references/zreferences

glossary/glossary

Expand All @@ -67,3 +67,5 @@ AliceVision is a Photogrammetric Computer Vision Framework which provides 3D Rec
:caption: About

about/about
copyright

Binary file not shown.
11 changes: 7 additions & 4 deletions source/node-reference/nodes/CameraRigCalibration.rst
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Expand Up @@ -36,9 +36,10 @@ https://www.researchgate.net/publication/327513182\ *Camera*\ Rig\ *Extrinsic*\

https://alicevision.github.io/#photogrammetry/localization

[Kneip2011] A Novel Parametrization of the Perspective-Three-Point
Problem for a Direct Computation of Absolute Camera Position and
Orientation. L. Kneip, D. Scaramuzza, R. Siegwart. June 2011

**References**

:cite:`Kneip2011`

[Kneip2013] Using Multi-Camera Systems in Robotics: Efficient Solutions
to the NPnP ProblemL. Kneip, P. Furgale, R. Siegwart. May 2013
Expand All @@ -49,7 +50,7 @@ calibrated geometric vision, L. Kneip, P. Furgale. May 2014.
[Kneip2014] Efficient Computation of Relative Pose for Multi-Camera
Systems. L. Kneip, H. Li. June 2014

settings
**Settings**

===================== ======================================================================================================================================================================================
Name Description
Expand Down Expand Up @@ -83,3 +84,5 @@ Output File The name of the file where to store the calibration data (
matching performances. It can be downloaded here.
https://github.com/fragofer/voctree You need to specify the path to
vlfeat_K80L3.SIFT.tree in **Voctree**.


14 changes: 7 additions & 7 deletions source/node-reference/nodes/FeatureExtraction.rst
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Expand Up @@ -10,13 +10,13 @@ This step extracts features from the images, as well as descriptors for those fe
======================== ===========================================================================================================================================================================
Name Description
======================== ===========================================================================================================================================================================
Input SfMData file.
Describer Types Describer types used to describe an image. **'sift'**, 'sift*float', 'sift*\ upright', 'akaze', 'akaze*liop', 'akaze*\ mldb', 'cctag3', 'cctag4', 'sift*ocv', 'akaze*\ ocv'
Describer Preset Control the ImageDescriber configuration (low, medium, **normal**, high, ultra). Configuration "ultra" can take long time !
Force CPU Extraction Use only CPU feature extraction.
Max Nb Threads Specifies the maximum number of threads to run simultaneously (0 for automatic mode). (0-24) **0**
Verbose Level verbosity level (fatal, error, warning, info, debug, trace).
Output Folder Output path for the features and descriptors files (*.feat, \*.desc).
Input SfMData file.
Describer Types Describer types used to describe an image. **'sift'**, 'sift*float', 'sift*\ upright', 'akaze', 'akaze*liop', 'akaze*\ mldb', 'cctag3', 'cctag4', 'sift*ocv', 'akaze*\ ocv'
Describer Preset Control the ImageDescriber configuration (low, medium, **normal**, high, ultra). Configuration "ultra" can take long time !
Force CPU Extraction Use only CPU feature extraction.
Max Nb Threads Specifies the maximum number of threads to run simultaneously (0 for automatic mode). (0-24) **0**
Verbose Level verbosity level (fatal, error, warning, info, debug, trace).
Output Folder Output path for the features and descriptors files (\*.feat, \*.desc).
======================== ===========================================================================================================================================================================


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8 changes: 4 additions & 4 deletions source/node-reference/nodes/MeshDenoising.rst
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Expand Up @@ -34,10 +34,10 @@ Wang et al.
``https://dl.acm.org/citation.cfm?id=2818068``

.. |image0| image:: mesh-denoising.jpg
:target: mesh-denoising.jpg
:target: mesh-denoising.jpg

**Detailed Description**
**Detailed Description**

A larger value of Lambda or Eta leads to a smoother filtering result.
A larger value of Lambda or Eta leads to a smoother filtering result.

From: "Static/Dynamic Filtering for Mesh Geometry" by Zhang Et al. https://arxiv.org/pdf/1712.03574.pdf
From: "Static/Dynamic Filtering for Mesh Geometry" by Zhang Et al. https://arxiv.org/pdf/1712.03574.pdf
56 changes: 28 additions & 28 deletions source/node-reference/nodes/Meshing.rst
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Expand Up @@ -8,34 +8,34 @@ Generate Mesh from SfM point cloud or DepthMap
=================================================== ==============================================================================================================================================================================================================================================
Name Description
=================================================== ==============================================================================================================================================================================================================================================
Input SfMData file.
Depth Maps Folder Input depth maps folder
Filtered Depth Maps Folder Input filtered depth maps folder
Estimate Space From SfM Estimate the 3d space from the SfM
Min Observations For SfM Space Estimation Minimum number of observations for SfM space estimation. (0-100) **3**
Min Observations Angle For SfM Space Estimation Minimum angle between two observations for SfM space estimation. (0-120) **10**
Max Input Points Max input points loaded from depth map images (500**000** - 500\ **000**\ 000)
Max Points Max points at the end of the depth maps fusion (100**000** - 10\ **000**\ 000)
Max Points Per Voxel (500**000** – 30\ **000**\ 000)
Min Step The step used to load depth values from depth maps is computed from maxInputPts. Here we define the minimal value for this step, so on small datasets we will not spend too much time at the beginning loading all depth values (1- 20) **2**
Partitioning (**singleBlock**, auto)
Repartition (**multiResolution**, regularGrid)
angleFactor (0.0-200.0) **15.0**
simFactor (0.0-200.0) **1.0**
pixSizeMarginInitCoef (0.0-10.0) **2.0**
pixSizeMarginFinalCoef (0.0-10.0) **4.0**
voteMarginFactor (0.1-10.0) **4.0**
contributeMarginFactor (0.0-10.0) **2.0**
simGaussianSizeInit (0.0-50) **10.0**
simGaussianSize (0.0-50) **0.1**
minAngleThreshold (0.0-10.0) **0.01**
Refine Fuse Refine depth map fusion with the new pixels size defined by angle and similarity scores.
Add Landmarks To The Dense Point Cloud Add SfM Landmarks to the dense point cloud.
Colorize Output Whether to colorize output dense point cloud and mesh.
Save Raw Dense Point Cloud Save dense point cloud before cut and filtering.
Verbose Level verbosity level (fatal, error, warning, **info**, debug, trace).
Output Mesh Output mesh (OBJ file format). mesh.obj
Output Dense Point Cloud Output dense point cloud with visibilities (SfMData file format). densePointCloud.abc
Input SfMData file.
Depth Maps Folder Input depth maps folder
Filtered Depth Maps Folder Input filtered depth maps folder
Estimate Space From SfM Estimate the 3d space from the SfM
Min Observations For SfM Space Estimation Minimum number of observations for SfM space estimation. (0-100) **3**
Min Observations Angle For SfM Space Estimation Minimum angle between two observations for SfM space estimation. (0-120) **10**
Max Input Points Max input points loaded from depth map images (500**000** - 500\ **000**\ 000)
Max Points Max points at the end of the depth maps fusion (100**000** - 10\ **000**\ 000)
Max Points Per Voxel (500**000** – 30\ **000**\ 000)
Min Step The step used to load depth values from depth maps is computed from maxInputPts. Here we define the minimal value for this step, so on small datasets we will not spend too much time at the beginning loading all depth values (1- 20) **2**
Partitioning (**singleBlock**, auto)
Repartition (**multiResolution**, regularGrid)
angleFactor (0.0-200.0) **15.0**
simFactor (0.0-200.0) **1.0**
pixSizeMarginInitCoef (0.0-10.0) **2.0**
pixSizeMarginFinalCoef (0.0-10.0) **4.0**
voteMarginFactor (0.1-10.0) **4.0**
contributeMarginFactor (0.0-10.0) **2.0**
simGaussianSizeInit (0.0-50) **10.0**
simGaussianSize (0.0-50) **0.1**
minAngleThreshold (0.0-10.0) **0.01**
Refine Fuse Refine depth map fusion with the new pixels size defined by angle and similarity scores.
Add Landmarks To The Dense Point Cloud Add SfM Landmarks to the dense point cloud.
Colorize Output Whether to colorize output dense point cloud and mesh.
Save Raw Dense Point Cloud Save dense point cloud before cut and filtering.
Verbose Level verbosity level (fatal, error, warning, **info**, debug, trace).
Output Mesh Output mesh (OBJ file format). mesh.obj
Output Dense Point Cloud Output dense point cloud with visibilities (SfMData file format). densePointCloud.abc
=================================================== ==============================================================================================================================================================================================================================================


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References
==========

**Bibliography**

.. bibliography:: ../refs.bib


**Text publications**

...
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12 changes: 12 additions & 0 deletions source/refs.bib
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@inproceedings{Kneip2011,
author = {Kneip, Laurent and Scaramuzza, Davide and Siegwart, Roland},
year = {2011},
month = {06},
pages = {2969-2976},
title = {A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation},
booktitle = {CVPR, IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
journal = {Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2011.5995464}
}

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