![]() This section includes descriptions of each module and component in the codebase. Develop a better understanding of the core of our technology and terminology. □ Reference: describes each class and function. Learn how to set up a model pipeline, use the viewer, create a custom config, and more. □ Developer Guides: describe all of the components and additional support we provide to help you construct, train, and debug your NeRFs. We’ve provided some interactive notebooks that walk you through what each component is all about. □ Nerfology: want to learn more about the tech itself? We’re here to help with our educational guides. Contains a quick tour, installation, and an overview of the core structures that will allow you to get up and running with nerfstudio. □♀️ Getting Started: a great place to start if you are new to nerfstudio. This documentation is organized into 3 parts: We hope nerfstudio enables you to build faster □ learn together □ and contribute to our NeRF community □. 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! Please do not hesitate to reach out to the nerfstudio team with any questions via Discord. Have feature requests? Want to add your brand-spankin’-new NeRF model? Have a new dataset? We welcome contributions! So we’re here to help with tutorials, documentation, and more! As researchers, we know just how hard it is to get onboarded with this next-gen technology. 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. It is currently developed by Berkeley students and community contributors. 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). This is a contributor-friendly repo with the goal of building a community where users can more easily build upon each other’s contributions. With more modular NeRFs, we hope to create a more user-friendly experience in exploring the technology. The library supports a more interpretable implementation of NeRFs by modularizing each component. Nerfstudio provides a simple API that allows for a simplified end-to-end process of creating, training, and testing NeRFs.
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