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Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction pipeline

Shell 0.04% CMake 2.11% C++ 90.89% C 0.72% Python 0.34% Objective-C 0.02% Cuda 5.59% MATLAB 0.04% GLSL 0.01% Objective-C++ 0.03% CSS 0.12% HTML 0.01% Emacs Lisp 0.03% QMake 0.05%

visualinertialkinectfusion's Introduction

Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline

CVPR'18 Workshop on Visual Odometry and Computer Vision Applications Based on Location Clues

Available at openaccess.thecvf.com

@InProceedings{Giancola_2018_CVPR_Workshops,
  author = {Giancola, Silvio and Schneider, Jens and Wonka, Peter and Ghanem, Bernard S.},
  title = {Integration of Absolute Orientation Measurements in the KinectFusion Reconstruction Pipeline},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  month = {June},
  year = {2018}
}

Implementation

Tested with:

VTK 6.2,
Boost 1.58,
Eigen 3,
FLANN,
Qt 5.9

Install a modified version of PCL

mkdir pcl-build && cd pcl-build/
cmake -DBUILD_GPU=ON -DBUILD_CUDA=ON -DWITH_QT=OFF ../pcl
make -j8
sudo make install
cd ..

Install a modified version of PCViewer

mkdir PCViewer-build && cd PCViewer-build
cmake ../PCViewer
make -j8 testKinFuGUI
cd ..

Create dataset

Download the sequences from the freiburg dataset
Extract the sequences into the data folder
Activate the conda environment conda env create -f environment.yml && conda activate TUMdataset
Copy python script into sequence folder cp *.py {sequencename} && cd {sequencename} with {sequencename} being the name of the folder for a given sequence
Extract the orientated point cloud mkdir pointclouds && python generate_registered_pointcloud_organized.py rgb.txt depth.txt groundtruth.txt pointclouds/PC --pcd_format cd ../..