Implementation of Newcombe et al. 2015 DynamicFusion paper.

The code is based on this KinectFusion implemenation

Dependencies:

• CUDA 5.0 or higher
• OpenCV 2.4.8 or higher (modules opencv_core, opencv_highgui, opencv_calib3d, opencv_imgproc, opencv_viz). Make sure that WITH_VTK flag is enabled in CMake during OpenCV configuration.
• Nanoflann (included in the repository)
• Boost (libraries system, filesystem and program options. Only used in the demo. Tested with 1.64.0)
• Ceres solver (Tested with version 1.13.0)

Implicit dependency (needed by opencv_viz):

• VTK 5.8.0 or higher
• SuiteSparse, BLAS and LAPACK for ceres Optional dependencies:
• GTest for testing
• Doxygen for documentation
• OpenNI v1.5.4 for getting input straight from a kinect device.

Install NVIDIA drivers.A good tutorial with some common issues covered can be found here.

Download and install CUDA from the NVIDIA website.

Install VTK, SuiteSparse, BLAS and LAPACK

sudo apt-get install libvtk5-dev libsuitesparse-dev liblapack-dev libblas-dev

Clone Ceres Solver git clone https://ceres-solver.googlesource.com/ceres-solver, then build and install using cmake and make.

Clone and install opencv. The project should work with any version above 2.4.8

git clone https://github.com/opencv/opencv
cd opencv
git checkout 3.2.0
mkdir build
cd build
cmake .. -DWITH_VTK=ON -DBUILD_opencv_calib3d=ON -DBUILD_opencv_imgproc=ON
make -j4
sudo make install

Get Boost 1.64.0 or above. Don't install it through apt-get as it sometimes results in linking errors on Ubuntu 16.04.

Optionals: Doxygen can be installed through apt-get: sudo apt-get install doxygen. Clone and install GTest Clone and install OpenNI

Now clone the repository and build:

git clone https://github.com/mihaibujanca/dynamicfusion
mkdir build
cd build
cmake ..
make -j4

If you want to build the tests as well, make sure you pass -DBUILD_TESTS=ON to the cmake command.

To build documentation, go to the project root directory and execute

doxygen -g
doxygen Doxyfile

• Install NVIDIA drivers and CUDA
• Install LAPACK.
• Install VTK (download and build from source)
• Install OpenCV.
• Install Boost

Optionals:

• Doxygen
• GTest
• OpenNI

Download an example dataset using ./download_data. To run the project, use ./build/bin/dynamicfusion <project_root>/data/umbrella

Download the dataset.
Create a data folder inside the project root directory.
Unzip the archive into data and remove any files that are not .png.
Inside data, create directories color and depth, and move color and depth frames to their corresponding folders.

To use with .oni captures or straight from a kinect device, use ./build/bin/dynamicfusion_kinect <path-to-oni> or ./build/bin/dynamicfusion_kinect <device_id>

Note: currently, the frame rate is too low (5-6fps) to be able to cope with live inputs, so it is advisable that you capture your input first.

DynamicFusion project page

@InProceedings{Newcombe_2015_CVPR,
author = {Newcombe, Richard A. and Fox, Dieter and Seitz, Steven M.},
title = {DynamicFusion: Reconstruction and Tracking of Non-Rigid Scenes in Real-Time},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}

The example dataset is taken from the VolumeDeform project.

@inbook{innmann2016volume,
author = "Innmann, Matthias and Zollh{\"o}fer, Michael and Nie{\ss}ner, Matthias and Theobalt, Christian 
         and Stamminger, Marc",
editor = "Leibe, Bastian and Matas, Jiri and Sebe, Nicu and Welling, Max",
title = "VolumeDeform: Real-Time Volumetric Non-rigid Reconstruction",
bookTitle = "Computer Vision -- ECCV 2016: 14th European Conference, Amsterdam, The Netherlands,
            October 11-14, 2016, Proceedings, Part VIII",
year = "2016",
publisher = "Springer International Publishing",
address = "Cham",
pages = "362--379",
isbn = "978-3-319-46484-8",
doi = "10.1007/978-3-319-46484-8_22",
url = "http://dx.doi.org/10.1007/978-3-319-46484-8_22"
}