FastNeRF: High-Fidelity Neural Rendering at 200FPS

TL;DR

FastNeRF achieves real-time, high-fidelity neural rendering at 200Hz by introducing a graphics-inspired factorization that significantly accelerates NeRF rendering, enabling interactive applications on consumer hardware.

Contribution

The paper presents FastNeRF, a novel NeRF-based system that dramatically speeds up rendering while maintaining quality, suitable for mobile and mixed reality devices.

Findings

3000 times faster than original NeRF

Achieves 200Hz rendering on high-end GPU

Maintains visual quality comparable to original NeRF

Abstract

Recent work on Neural Radiance Fields (NeRF) showed how neural networks can be used to encode complex 3D environments that can be rendered photorealistically from novel viewpoints. Rendering these images is very computationally demanding and recent improvements are still a long way from enabling interactive rates, even on high-end hardware. Motivated by scenarios on mobile and mixed reality devices, we propose FastNeRF, the first NeRF-based system capable of rendering high fidelity photorealistic images at 200Hz on a high-end consumer GPU. The core of our method is a graphics-inspired factorization that allows for (i) compactly caching a deep radiance map at each position in space, (ii) efficiently querying that map using ray directions to estimate the pixel values in the rendered image. Extensive experiments show that the proposed method is 3000 times faster than the original NeRF algorithm and at least an order of magnitude faster than existing work on accelerating NeRF, while maintaining visual quality and extensibility.