See the python notebook neural_radiosity.ipynb.

Test environment

• Ubuntu 20.4
• NVIDIA driver version 515.86.01
• CUDA driver version 11.7
• CUDA runtime version 11.7
• GCC >= 8
• CMake >= 3.21

git clone https://github.com/krafton-ai/neural-radiosity-tutorial-mitsuba3.git
python3 -m pip install --upgrade pip
pip install -r requirements.txt

To install Tiny-CUDA-NN, check the official documentation. In short,

# but first, you may need to update CUDA toolkit and CMake as the following sections
apt-get install build-essential git python3-dev python3-pip libopenexr-dev \ 
    libxi-dev libglfw3-dev libglew-dev libomp-dev libxinerama-dev libxcursor-dev \ 
    ffmpeg
pip install "git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch"

# if you encounter something like 'no LLVM.so' error,
apt-get update
apt-get -y install llvm
find / -iname libLLVM.so  # verify the installation
pip uninstall mitsuba drjit
pip install -r requirements.txt

1. Go to Microsoft Store and install Windows Subsystem for Linux (WSL). See here for more detailed information.
2. Go to Microsoft Store and install VSCode.
3. Open a VSCode window, and install the WSL extension from VSCode. See here if you are not aware of VSCode Extensions.
4. In a VSCode window, press ctrl+shift+p and write WSL to find WSL: Connect to WSL option. You can now open a VSCode window on a virtualized Ubuntu environment by pressing the option. See here if you are not aware of the ctrl+shift+p command in VSCode.
5. The remaining steps are the same as in the Installation section above.

• For instance, Tiny-CUDA-NN requires a recent verion of CUDA toolkit which is 11.5 or higher.
• To install, CUDA toolkit 11.7, follow the link
• You can also find other versions of toolkits in the NVIDIA archive
• Anyway the above 11.7 link will introduce the following installation instructions:

wget https://developer.download.nvidia.com/compute/cuda/11.7.0/local_installers/cuda_11.7.0_515.43.04_linux.run
sh cuda_11.7.0_515.43.04_linux.run

# 1. accept the license stuff
# 2. uncheck `Driver`, `CUDA Samples xx.x`, `CUDA Demo Suite xx.x`, `CUDA Documentation  xx.x`.
#    we only need 'CUDA Toolkit xx.x`

• After the installation is finished, open ~/.bashrc and add some paths as follow.

...
export PATH=/usr/local/cuda-11.7/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-11.7/lib64

• Get the CMake binary for Linux such as cmake-3.26.0-rc6-linux-x86_64.sh.
• Install the binary by hitting sh cmake-3.26.0-rc6-linux-x86_64.sh.
• Add some environment variables

...
export PATH=~/cmake-3.26.0-rc6-linux-x86_64/bin:$PATH
export CMAKE_PREFIX_PATH=~/cmake-3.26.0-rc6-linux-x86_64:$CMAKE_PREFIX_PATH

The code is released under the Apache-2.0 License. See LICENSE for full terms.