nerfstudio

Introducing Nerfstudio: A Friendly Studio for NeRF

Overview

Nerfstudio is a user-friendly, collaborative platform designed to simplify the creation, training, and evaluation of Neural Radiance Fields (NeRFs). It offers a modular approach to NeRFs, making the exploration of this technology accessible to both beginners and seasoned developers. This open-source project was launched by students from Berkeley's KAIR lab under the Berkeley AI Research (BAIR) initiative.

Key Features

Nerfstudio shines in its ability to make NeRF technology easily approachable through its modular implementation. By dividing the components of NeRF into understandable segments, it enhances the user experience. The community-driven project aims to foster a collaborative environment where contributions and advancements can be shared and built upon by everyone.

Getting Started

Installation

To embark on a journey with Nerfstudio, you need an NVIDIA video card with CUDA installed (tested with version 11.8). The recommended way to manage this is via Conda:

1. Set up a Python environment:

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

2. Install necessary dependencies including PyTorch and CUDA:

   pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 --extra-index-url https://download.pytorch.org/whl/cu118
   conda install -c "nvidia/label/cuda-11.8.0" cuda-toolkit
   pip install ninja git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
   

3. Install Nerfstudio:

   For the latest version:

   pip install nerfstudio
   

   Or clone the repository for the most recent updates:

   git clone https://github.com/nerfstudio-project/nerfstudio.git
   cd nerfstudio
   pip install --upgrade pip setuptools
   pip install -e .
   

Training Your First Model

Training your first model involves setting the path to training data and executing a training script:

1. Download test data and start training:

   ns-download-data nerfstudio --capture-name=poster
   ns-train nerfacto --data data/nerfstudio/poster
   

This process should start showing the training progress in your terminal, including a link to view the training status on the web.

Advanced Features

Using Custom Data

You can utilize your own datasets with Nerfstudio by converting data using ns-process-data. Supported types include images, video, 360-degree data, and mobile captures from apps like Polycam and Record3D.

Visualize and Export

Once you have trained your models, use Nerfstudio to visualize them or export your results as videos or point clouds. This includes creating a custom camera path to render scenes.

Support and Community

Nerfstudio has a vibrant community and is partly funded by sponsors such as Luma AI and the BAIR commons. They welcome contributions and support user engagement via Discord and other community forums. Feedback and contributions are always appreciated and can immensely help in the evolution of the platform.

Conclusion

Nerfstudio stands out as an inviting platform for anyone interested in delving into NeRF technology. Its simplified, modular structure and supportive community pave the way for groundbreaking innovations in neural rendering. Whether you're a researcher, developer, or enthusiast, Nerfstudio has the resources and community support to help you succeed.

For more detailed information and documentation, visit Nerfstudio Documentation.