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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.
Features
- Contains a quick tour, installation, and an overview of the core structures that will allow you to get up and running with nerfstudio
- Documentation available
- We’ve provided some interactive notebooks that walk you through what each component is all about
- Describe all of the components and additional support we provide to help you construct, train, and debug your NeRFs
- Set up a model pipeline, use the viewer, create a custom config, and more
- Develop a better understanding of the core of our technology and terminology
Project Samples
