SIBR Core

SIBR is a System for Image-Based Rendering.
It is built around the sibr-core in this repo and several Projects implementing published research papers.
For more complete documentation, see here: SIBR Documentation

This SIBR core repository provides :

• a basic Image-Based Renderer
• a per-pixel implementation of Unstructured Lumigraph (ULR)
• several dataset tools & pipelines do process input images

Details on how to run in the documentation and in the section below.
If you use this code in a publication, please cite the system as follows:

@misc{sibr2020,
   author       = "Bonopera, Sebastien and Esnault, Jerome and Prakash, Siddhant and Rodriguez, Simon and Thonat, Theo and Benadel, Mehdi and Chaurasia, Gaurav and Philip, Julien and Drettakis, George",
   title        = "sibr: A System for Image Based Rendering",
   year         = "2020",
   url          = "https://gitlab.inria.fr/sibr/sibr_core"
}

Setup

Note: The current release is for Windows 10 only. We are planning a Linux release soon.

Binary distribution

The easiest way to use SIBR is to download the binary distribution. All steps described below, including all preprocessing for your datasets will work using this code.

Download the distribution from the page: https://sibr.gitlabpages.inria.fr/download.html (Core, 57Mb); unzip the file and rename the directory "install".

Install requirements

• Visual Studio 2019
• Cmake 3.16+
• 7zip
• Python 3.8+ for shaders installation scripts and dataset preprocess scripts
• Doxygen 1.8.17+ for documentation
• CUDA 10.1+ and CUDnn if projects requires it

Make sure Python, CUDA and Doxygen are in the PATH

If you have Chocolatey, you can grab most of these with this command:

choco install cmake 7zip python3 doxygen.install cuda

## Visual Studio is available on Chocolatey,
## though we do advise to set it from Visual Studio Installer and to choose your licensing accordingly
choco install visualstudio2019community

Generation of the solution

• Checkout this repository's master branch:

  ## through HTTPS
  git clone https://gitlab.inria.fr/sibr/sibr_core.git -b master
  ## through SSH
  git clone git@gitlab.inria.fr:sibr/sibr_core.git -b master

• Run Cmake-gui once, select the repo root as a source directory, build/ as the build directory. Configure, select the Visual Studio C++ Win64 compiler

• Select the projects you want to generate among the BUILD elements in the list (you can group Cmake flags by categories to access those faster)

• Generate

Compilation

• Open the generated Visual Studio solution (build/sibr_projects.sln)
• Build the ALL_BUILD target, and then the INSTALL target
• The compiled executables will be put in install/bin
• TODO: are the DLLs properly installed?

Compilation of the documentation

• Open the generated Visual Studio solution (build/sibr_projects.sln)
• Build the DOCUMENTATION target
• Run install/docs/index.html in a browser

Scripts

Some scripts will require you to install PIL, and convert from ImageMagick.

## To install pillow
python -m pip install pillow

## If you have Chocolatey, you can install imagemagick from this command
choco install imagemagick

Troubleshooting

Bugs and Issues

We will track bugs and issues through the Issues interface on gitlab. Inria gitlab does not allow creation of external accounts, so if you have an issue/bug please email sibr@inria.fr and we will either create a guest account or create the issue on our side.

Cmake complaining about the version

if you are the first to use a very recent Cmake version, you will have to update CHECKED_VERSION in the root CmakeLists.txt.

Weird OpenCV error

you probably selected the 32-bits compiler in Cmake-gui.

Cmd.exe failed with error 009 or similar

make sure Python is installed and in the path.

BUILD_ALL or INSTALL fail because of a project you don't really need

build and install each project separately by selecting the proper targets.

Error in CUDA headers under Visual Studio 2019

make sure CUDA >= 10.1 (first version to support VS2019) is installed.

To run an example

For more details, please see the documentation: http://sibr.gitlabpages.inria.fr

Download a dataset from: https://repo-sam.inria.fr/fungraph/sibr-datasets/

e.g., the sibr-museum-front dataset in the DATASETS_PATH directory.

wget https://repo-sam.inria.fr/fungraph/sibr-datasets/museum_front27_ulr.zip

Once you have built the system or downloaded the binaries (see above), go to install/bin and you can run:

	sibr_ulrv2_app.exe --path DATASETS_PATH/sibr-museum-front

You will have an interactive viewer and you can navigate freely in the captured scene. Our default interactive viewer has a main view running the algorithm and a top view to visualize the position of the calibrated cameras. By default you are in WASD mode, and can toggle to trackball using the "y" key. Please see the page Interface for more details on the interface.

Please see the documentation on how to create a dataset from your own scene, and the various other IBR algorithms available.