UC Berkeley Researchers Introduce Nerfstudio: A Python Framework for Neural Radiance Field (NeRF) Development

Who isn’t a fan of Iron Man? He looks really cool when he is working in his lab. All the holograms and new gadgets he uses make him look cool. Is it possible to create such a 3D navigable scene (like a hologram)  from a 2D photograph? Researchers at UC Berkeley succeeded in doing it using a technology called Neural Radiance Fields (NeRF). Other researchers at Berkley also created a development framework to speed up the NeRF projects to make them more accessible.

Due to the wide range of applications in computer vision, graphics, and robotics, the development of NeRF is rapidly growing. The researchers at Berkley propose a modular PyTorch framework that includes plug-and-play components for implementing NeRF-based methods in various projects. Their modular design also supports real-time visualization tools and tools for exporting to video, point cloud, and mesh representations. 

Rapid development in NeRF has led to many research papers being published but tracking the progress of this is difficult due to a lack of code consolidation. Many papers implement features in their own siloed repository which complicates the process of transferring features and research contributions across different implementations. To resolve this issue, researchers at Berkley present consolidated NeRF innovatives as Nerfstudios. The major goals of Nerfstudios are to consolidate various NeRF techniques into reusable, modular components and enable real-time visualization of NeRF scenes with a rich suite of controls. This will provide an easy-to-use workflow for creating NeRFs from user-captured data.

Nerfstudios consists of a real-time visualizer hosted on the web to work with any model during training or testing. This makes it accessible without requiring a local GPU machine. This also supports different images clicked from the various camera types and mobile applications like Polycam, Record3D, and KIRI Engine. 

Nerfstudios real-time visualization interface is handy in the qualitative analysis of a model. This will allow more informed decisions during method development. For views that are far away from the capture trajectory, compared to PSNR, NeRF provides a comprehensive understanding of performance. Qualitative analysis is important because this allows the developer to gain a more holistic understanding of the model performance.

For an imposed image, Nerfstudios optimizes the 3D scene based on the radiance, density, and other quantities like semantics, normals, features, etc. These are input into a Data Manager followed by a model. The data manager deals with parsing image formats via a DataParser and generating rays as RayBundles. These Ray Bundles are input into a Model which will query Fields and render quantities. 

The researcher’s future work includes the development of more appropriate evaluation matrices and the integration of the framework with other areas such as computer vision, computer graphics, and machine learning. The development of NeRF-based methods accelerates the advances in the neural rendering community. 

Check out the Paper. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 27k+ ML SubReddit, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.