论文标题
Mobilenerf:利用多边形栅格化管道,以在移动体系结构上进行有效的神经场渲染
MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures
论文作者
论文摘要
神经辐射场(NERF)表现出惊人的能力,可以从新颖的观点中综合3D场景的图像。但是,他们依赖于基于射线行进的专门体积渲染算法,这些算法与广泛部署的图形硬件的功能不匹配。本文介绍了基于纹理多边形的新NERF表示形式,该表示可以与标准渲染管道有效地合成新型图像。 NERF表示为一组多边形,其纹理代表二进制不相处和特征向量。用Z-buffer对多边形的传统渲染产生了每个像素的图像,该图像由在片段着色器中运行的小型,观点依赖的MLP来解释,以产生最终的像素颜色。这种方法使NERF可以通过传统的多边形栅格化管道渲染,该管道提供了庞大的像素级并行性,从而在包括移动电话在内的广泛计算平台上实现了交互式帧速率。
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.