git submodule update --init --recursive
mkdir build && cd build
cmake -DPYTHON_EXECUTABLE=${PATH_TO_YOUR_PYTHON_EXECUTABLE} ..
make -j4
cd ..
Clone the repository and its submodules:
git clone --recurse-submodules git@github.com:danini/robust-line-based-estimator.git
Make sure that you have the necessary OpenCV libraries installed:
sudo apt-get install libopencv-dev libopencv-contrib-dev libarpack++2-dev libarpack2-dev libsuperlu-dev
Install the necessary requirements, third party libraries, and pre-trained models:
bash install.sh
Data preparation is kept the same. And then run:
python runners/run_scannet.py
Download the data from the CVPR tutorial "RANSAC in 2020":
wget http://cmp.felk.cvut.cz/~mishkdmy/CVPR-RANSAC-Tutorial-2020/RANSAC-Tutorial-Data-EF.tar
tar -xf RANSAC-Tutorial-Data-EF.tar
Then run the notebook examples/relative_pose_evaluation_phototourism.ipynb.
Download the data from the test set for relative pose estimation used in SuperGlue (~250Mb for 1500 image pairs only):
wget https://www.polybox.ethz.ch/index.php/s/lAZyxm62WUh27Zl/download
unzip ScanNet_test.zip -d <path to extract the ScanNet test set>
Then run the notebook examples/relative_pose_evaluation_scannet.ipynb.
Download the 7Scenes dataset and put it where it suits you. You can also only download one scene and later specify this scene in the dataloader constructor.
Then run the script runners/run_7scenes.py.
Download the ETH3D dataset (training split of the high-res multi-view, undistorted images + GT extrinsics & intrinsics should be enough) and put it where it suits you. The input argument 'downsize_factor' can be used to downscale the images, because they can be quite large otherwise.
Then run the script runners/run_eth3d.py.
Download the CAB scene of the LaMAR dataset, and unzip it to your favourite location. Note that we only use the images in CAB/sessions/query_val_hololens.
Download the KITTI odometry dataset (grayscale images and poses), and unzip them to your favourite location.