- Prepare Host System (Ubuntu)
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt install g++-9
g++-9 --version # Should Print Version 9.4.0 or higher
./install_pytorch_precompiled.sh
conda activate neat
export CONDA=${CONDA_PREFIX:-"$(dirname $(which conda))/../"}
export CC=gcc-9
export CXX=g++-9
export CUDAHOSTCXX=g++-9
mkdir build
cd build
cmake -DCMAKE_PREFIX_PATH="${CONDA}/lib/python3.8/site-packages/torch/;${CONDA}" ..
make -j10
- Get Pepper dataset from here: https://repository.kaust.edu.sa/handle/10754/676019
- Extract datasets
- Update the
main()
of nikon2neat.cpp
to point to the downloaded dataset directory (the output should be into NeAT/scenes)
- Preprocess data using our nikon2neat programm:
mkdir scenes
cd NeAT
export LD_LIBRARY_PATH=~/anaconda3/envs/neat/lib
./build/bin/nikon2neat
- Update configuration file in configs/
- Run reconstruction
cd NeAT
export LD_LIBRARY_PATH=~/anaconda3/envs/neat/lib
./build/bin/reconstruct configs/pepper.ini
- The result will be written to NeAT/Experiments
- Use tensorboard for easy visualization:
conda activate neat
cd NeAT
tensorboard --logdir Experiments/ --samples_per_plugin images=100