ref: https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Instant_Neural_Graphics_Primitives_with_a_Multiresolution_Hash_Encoding https://pytorch.org/tutorials/advanced/cpp_custom_ops.html https://pytorch.org/tutorials/advanced/cpp_extension.html https://github.com/pytorch/extension-cpp/ https://pytorch.org/docs/stable/notes/extending.html
https://pytorch.org/tutorials/intermediate/custom_function_double_backward_tutorial.html https://pytorch.org/docs/stable/notes/extending.html
ds: https://drive.google.com/drive/folders/1eO7DXFhWWpauC-9LDhOimtIKxY3yRCIm
in the nerf paper they used spherical harmonics for color instead
- dataset class
- tcnn hashing
- tcnn forward
- occupancy grid updates
- cuda ray marching
- implement a viewer for training
conda install -c conda-forge cuda=12.1 gxx python=3.11.8
pip3 install torch torchvision torchaudio #torch 2.4.1 cu12.1
pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
pip install torch-scatter -f https://data.pyg.org/whl/torch-2.4.1+cu121.html
pip install einops kornia matplotlib opencv-python lpips imageio imageio-ffmpeg scipy pymcubes trimesh dearpygui lightning