· 2023
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
- 279citations
- 2023year
Short summary
MobileNeRF represents Neural Radiance Fields (NeRFs) as textured polygons, enabling efficient rendering on mobile devices using standard graphics pipelines and achieving interactive frame rates.
AI-generated from the title and abstract; the full text is not read.
Key points
- NeRFs are represented as textured polygons with binary opacities and feature vectors.
- Rendering utilizes the traditional polygon rasterization pipeline with a z-buffer.
- A view-dependent MLP in the fragment shader generates final pixel colors.
- Achieves interactive frame rates on mobile devices and other compute platforms.
AI-generated from the title and abstract; the full text is not read.
Abstract
Neural Radiance Fields (NeRFs) have demonstrated amazing ability to synthesize images of 3D scenes from novel views. However, they rely upon specialized volumetric rendering algorithms based on ray marching that are mismatched to the capabilities of widely deployed graphics hardware. This paper introduces a new NeRF representation based on textured polygons that can synthesize novel images efficiently with standard rendering pipelines. The NeRF is represented as a set of polygons with textures representing binary opacities and feature vectors. Traditional rendering of the polygons with a z-buffer yields an image with features at every pixel, which are interpreted by a small, view-dependent MLP running in a fragment shader to produce a final pixel color. This approach enables NeRFs to be rendered with the traditional polygon rasterization pipeline, which provides massive pixel-level parallelism, achieving interactive frame rates on a wide range of compute platforms, including mobile phones. Project page: https://mobile-nerf.github.io
The authors' abstract, as published at the source., 2023 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science