Ever wanted to train a NeRF model of a fox in under 5 seconds? Or fly around a scene captured from photos of a factory robot? Of course you have!

Here you will find an implementation of four neural graphics primitives, being neural radiance fields (NeRF), signed distance functions (SDFs), neural images, and neural volumes. In each case, we train and render a MLP with multiresolution hash input encoding using the tiny-cuda-nn framework.

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller
arXiv:2201.05989 [cs.CV], Jan 2022
Project page / Paper / Video / BibTeX

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• An NVIDIA GPU; tensor cores increase performance when available. All shown results come from an RTX 3090.
• A C++14 capable compiler. The following choices are recommended and have been tested:
  • Windows: Visual Studio 2019
  • Linux: GCC/G++ 7.5 or higher
• CUDA v10.2 or higher and CMake v3.21 or higher.
• (optional) Python 3.7 or higher for interactive bindings. Also, run pip install -r requir