README

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 A collaboration friendly studio for NeRFs 


    
        
    
        
    
        


 

- Quickstart
- Learn more
- Supported Features

 About

_It’s as simple as plug and play with nerfstudio!_

Nerfstudio provides a simple API that allows for a simplified end-to-end process of creating, training, and testing NeRFs.
The library supports a more interpretable implementation of NeRFs by modularizing each component.
With more modular NeRFs, we hope to create a more user-friendly experience in exploring the technology.

This is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other's contributions.
Nerfstudio initially launched as an opensource project by Berkeley students in KAIR lab at Berkeley AI Research (BAIR) in October 2022 as a part of a research project (paper). It is currently developed by Berkeley students and community contributors.

We are committed to providing learning resources to help you understand the basics of (if you're just getting started), and keep up-to-date with (if you're a seasoned veteran) all things NeRF. As researchers, we know just how hard it is to get onboarded with this next-gen technology. So we're here to help with tutorials, documentation, and more!

Have feature requests? Want to add your brand-spankin'-new NeRF model? Have a new dataset? We welcome contributions! Please do not hesitate to reach out to the nerfstudio team with any questions via Discord.

Have feedback? We'd love for you to fill out our Nerfstudio Feedback Form if you want to let us know who you are, why you are interested in Nerfstudio, or provide any feedback!

We hope nerfstudio enables you to build faster :hammer: learn together :books: and contribute to our NeRF community :sparkling_heart:.

 Sponsors

Sponsors of this work includes Luma AI and the BAIR commons.


    
        
        
        
        
        
        
        
        
        
    
    
        
        
        
        
        
        
        
        
        
    


 Quickstart

The quickstart will help you get started with the default vanilla NeRF trained on the classic Blender Lego scene.
For more complex changes (e.g., running with your own data/setting up a new NeRF graph), please refer to our references.

 1. Installation: Setup the environment

 Prerequisites

You must have an NVIDIA video card with CUDA installed on the system. This library has been tested with version 11.8 of CUDA. You can find more information about installing CUDA here

 Create environment

Nerfstudio requires python = 3.8. We recommend using conda to manage dependencies. Make sure to install Conda before proceeding.

bash
conda create --name nerfstudio -y python=3.8
conda activate nerfstudio
pip install --upgrade pip


 Dependencies

Install PyTorch with CUDA (this repo has been tested with CUDA 11.7 and CUDA 11.8) and tiny-cuda-nn.
cuda-toolkit is required for building tiny-cuda-nn.

For CUDA 11.8:

bash
pip install