nerfacc - Streamlined PyTorch Toolkit for Speeding Up NeRF Training and Inference

Project Overview: NerfAcc

NerfAcc is an advanced toolbox designed to enhance the efficiency and performance of Neural Radiance Fields (NeRFs) using PyTorch. It focuses on accelerating the volumetric rendering process associated with radiance fields, offering a universally adaptable and plug-and-play solution for most NeRF applications. By integrating NerfAcc into existing projects with minimal code changes, users can experience substantial speed improvements during both training and inference phases of NeRFs.

Key Features

Efficient Sampling Techniques: NerfAcc provides sophisticated sampling methods that are computationally cost-effective, helping to identify surfaces within radiance fields efficiently.
Plug-and-Play Interface: With its pure Python interface and flexible APIs, NerfAcc integrates seamlessly with existing NeRF models.
Support for Latest NeRF Research: The toolbox accommodates recent NeRF advancements, thereby ensuring compatibility with cutting-edge research and developments.
Easy Installation: NerfAcc can be installed via PyPI, where it compiles the necessary CUDA code during runtime, or from the source, compiling at installation.

Installation Instructions

Basic Requirements

Before installing NerfAcc, make sure that PyTorch is installed on your machine. The toolbox is compatible with various PyTorch and CUDA versions.

Installation Methods

PyPI Installation: Simply run
```
pip install nerfacc
```
This will compile CUDA code on the first run.
Source Installation: Run
```
pip install git+https://github.com/nerfstudio-project/nerfacc.git
```
This approach compiles CUDA during installation.
Pre-built Wheels: For specific PyTorch and CUDA version combinations, pre-built wheels are available.

Usage Example

To use NerfAcc in a NeRF project, users need to define two main functions:

sigma_fn: Defines how to compute density at each sample, essential for the surface discovery process.
rgb_sigma_fn: Used for computing both color and density, integral to the differentiable volumetric rendering process.

This allows NerfAcc to be easily integrated into any user-defined radiance field rendering pipeline for faster execution.

Example Applications

NerfAcc can drastically accelerate NeRF training across various datasets and scenarios, including:

Static NeRFs: Such as Instant-NGP on NeRF-Synthetic and Mip-NeRF 360 datasets, with performance improvements seen in minutes.
Dynamic NeRFs: Including T-NeRF on the D-NeRF dataset.
Camera Optimization NeRFs: Like BARF on the NeRF-Synthetic dataset.

Integration with Other Projects

NerfAcc is already used in several notable projects and papers, demonstrating its versatility and effectiveness:

Codebases: Integration with frameworks like Nerftudio and Sdfstudio.
Research Papers: Used in projects like "Representing Volumetric Videos as Dynamic MLP Maps" for CVPR 2023.

Common Issues and Solutions

Some users might encounter import errors related to pre-built wheel and CUDA mismatches; ensuring PyTorch and CUDA versions match the NerfAcc version will solve these problems.

How to Cite

For academic or research use, please cite NerfAcc using the following reference:

@article{li2023nerfacc,
  title={NerfAcc: Efficient Sampling Accelerates NeRFs.},
  author={Li, Ruilong and Gao, Hang and Tancik, Matthew and Kanazawa, Angjoo},
  journal={arXiv preprint arXiv:2305.04966},
  year={2023}
}

NerfAcc stands out by providing a significant boost in rendering efficiency for NeRF applications, making it an invaluable resource for researchers and developers aiming to optimize their work with NeRF models.