Official implementation of SAT-NGP, as presented in our paper:
SAT-NGP : Unleashing Neural Graphics Primitives for Fast Relightable Transient-Free 3D reconstruction from Satellite Imagery (IGARSS 2024)
Camille Billouard 1, Dawa Derksen 1,
Emmanuelle Sarrazin 1
and Bruno Vallet 2
1CNES, 2Univ Gustave Eiffel, ENSG, IGN, LASTIG, F-94160
CPU/GPU | runs |
---|---|
AMD EPYC Milan 7713 / NVIDIA A100 | ✅ |
Intel Xeon E5-2698 / NVIDIA V100 | ❌ |
AMD EPYC 7742 / NVIDIA A100 | ✅ |
Intel Core i512400F / RTX 4060Ti | ✅ |
AMD EPYC Milan 7713 / NVIDIA A40 | ✅ |
conda create -p satngp -y python=3.8
conda activate MY_PATH/satngp
python -m pip install --upgrade pip
python -m pip install setuptools==69.5.1
conda install anaconda::libtiff -y
conda install libnvjpeg-dev -c nvidia -y
conda install -c conda-forge ncurses -y
conda install gdal==3.4.1 libgdal -y
conda install -c anaconda git -y
Install PyTorch with CUDA (this repo has been tested with CUDA 11.7) :
pip install torch==2.0.1+cu117 torchvision==0.15.2+cu117 --extra-index-url https://download.pytorch.org/whl/cu117
conda install -c "nvidia/label/cuda-11.7.1" cuda-toolkit -y
cd satngp
ln -s lib lib64 # we want to avoid problems when compiling some packages
export LD_LIBRARY_PATH="$PWD/lib64:$LD_LIBRARY_PATH"
export VENV_LIB_PATH="$PWD/lib64/python3.8/site-packages/"
LDFLAGS="-L$PWD/lib"
export CUDA_PATH=$PWD
cd ..
git clone https://github.com/Ellimac0/SAT-NGP.git
cd SAT-NGP/
# https://github.com/rusty1s/pytorch_scatter
mkdir dep_ext
cd dep_ext
git clone --branch pytorch_1_11 https://github.com/rusty1s/pytorch_scatter/
cd pytorch_scatter
pip install . -vvv # -vvv is verbose for debugging during installation
cd ../..
cd SAT-NGP
pip install -r requirements.txt
The data came from the DFC2019 dataset and the link below from SAT-NeRF
mkdir data
cd data
wget https://github.com/centreborelli/satnerf/releases/download/EarthVision2022/dataset.zip
unzip dataset.zip -d dataset
# in SAT-NGP
# may take a few minutes the first time, as the backend is compiled at .cache/torch_extensions/py38_cu117/
bash scripts/run_sat_ngp.sh data/dataset JAX_XXX 60000 1024
Accepted to IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2024.
@misc{billouard2024satngp,
title={SAT-NGP : Unleashing Neural Graphics Primitives for Fast Relightable Transient-Free 3D reconstruction from Satellite Imagery},
author={Camille Billouard and Dawa Derksen and Emmanuelle Sarrazin and Bruno Vallet},
year={2024},
eprint={2403.18711},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
This work was performed using HPC resources from CNES Computing Center (DOI 10.24400/263303/CNES C3). The authors would like to thank the Johns Hopkins University Applied Physics Laboratory and IARPA for providing the data used in this study, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee for organizing the Data Fusion Contest. A portion of this work was build on top of :
-
Credits to Jiaxiang Tang for excellent work :
@misc{torch-ngp, Author = {Jiaxiang Tang}, Year = {2022}, Note = {https://github.com/ashawkey/torch-ngp}, Title = {Torch-ngp: a PyTorch implementation of instant-ngp} }
the contributors of :
and authors of :