Nerfstudio: A Modular Framework for Neural Radiance Field Development

TL;DR

Nerfstudio presents a modular, PyTorch-based framework that unifies data handling, model components, real-time visualization, and export workflows to accelerate NeRF development on real-world data. The Nerfacto method demonstrates how assembling components from multiple papers yields fast, interactive NeRFs suitable for non-synthetic settings, aided by a web-based viewer for qualitative assessment. The framework emphasizes community-driven development with open-source licensing, real-world data benchmarking, and an extensible pipeline that supports future advances in neural rendering. This work facilitates rapid prototyping, easier collaboration, and broader applicability of NeRFs across industry and research domains through a flexible, end-to-end platform.

Abstract

Neural Radiance Fields (NeRF) are a rapidly growing area of research with wide-ranging applications in computer vision, graphics, robotics, and more. In order to streamline the development and deployment of NeRF research, we propose a modular PyTorch framework, Nerfstudio. Our framework includes plug-and-play components for implementing NeRF-based methods, which make it easy for researchers and practitioners to incorporate NeRF into their projects. Additionally, the modular design enables support for extensive real-time visualization tools, streamlined pipelines for importing captured in-the-wild data, and tools for exporting to video, point cloud and mesh representations. The modularity of Nerfstudio enables the development of Nerfacto, our method that combines components from recent papers to achieve a balance between speed and quality, while also remaining flexible to future modifications. To promote community-driven development, all associated code and data are made publicly available with open-source licensing at https://nerf.studio.

Figures (9)

• Figure 1: Pipeline components. Each NeRF method is implemented as a custom Pipeline. DataManagers process input images into bundles of rays (RayBundles) that get rendered by the Model to produce a set of NeRF outputs (RayOutputs). A dictionary of losses supervises the pipeline end-to-end.
• Figure 2: Nerfstudio Dataset. Our Nerfstudio Dataset contains 10 scenes: 4 phone captures with pinhole lenses and 6 mirrorless camera captures with a fisheye lens. We focus our efforts on real-world data, and these scenes can help benchmark progress.
• Figure 3: Sample representations. (Top) We define a frustum as a cone with a start and end. This region of space can be converted into Gaussians (bottom left) or point samples (bottom right) depending on the field input format.
• Figure 4: Web viewer design. A machine with a GPU (left) starts a NeRF training session. When a user navigates to to the hosted web viewer (right), the viewer client will establish WebSocket and WebRTC connections with the training session.
• Figure 5: Exporting videos and geometry. We make exporting videos (a) and geometry (b) easy with real-data captures. The left side shows the interactive camera trajectory editor, which allows animatable poses, FOVs, and speed, to eventually render videos of NeRF's outputs. On the right we show the cropping interface in the viewer and resulting export formats including point clouds, TSDFs, and textured meshes.