HashNeRF-pytorch

Why we included this project

Researchers who want to understand why Instant-NGP trains neural radiance fields so fast can work through this pure-PyTorch port without touching the original CUDA and C++ code. The multi-resolution hash encoding at the heart of the method is laid out in a readable, self-contained way, and the project builds on NeRF-pytorch, so anyone familiar with that codebase will recognize the structure immediately. Training a chair scene for about five thousand iterations, roughly ten minutes on a modest GPU like a 1050Ti, already yields crisp renderings. Extra losses for total variation and sparsity give you easy controls to smooth embeddings or clean up geometry, and because everything stays in Python, you can edit the architecture and trace what each piece contributes. That makes it a practical starting point for playing with NeRF ideas rather than fighting native code.