Neural Radiance Field (NeRF) has emerged as a leading technique for novel view synthesis, owing to its impressive photorealistic reconstruction and rendering capability. Nevertheless, achieving real-time NeRF rendering in large-scale scenes has presented challenges, often leading to the adoption of either intricate baked mesh representations with a substantial number of triangles or resource-intensive ray marching in baked representations. We challenge these conventions, observing that high-quality geometry, represented by meshes with substantial triangles, is not necessary for achieving photorealistic rendering quality. Consequently, we propose MixRT, a novel NeRF representation that includes a low-quality mesh, a view-dependent displacement map, and a compressed NeRF model. This design effectively harnesses the capabilities of existing graphics hardware, thus enabling real-time NeRF rendering on edge devices. Leveraging a highly-optimized WebGL-based rendering framework, our proposed MixRT attains real-time rendering speeds on edge devices (over 30 FPS at a resolution of 1280 x 720 on a MacBook M1 Pro laptop), better rendering quality (0.2 PSNR higher in indoor scenes of the Unbounded-360 datasets), and a smaller storage size (less than 80% compared to state-of-the-art methods).

翻译：摘要：神经辐射场（NeRF）凭借其出色的逼真重建与渲染能力，已成为新视角合成领域的领先技术。然而，在大规模场景中实现实时NeRF渲染仍面临挑战，通常需要采用包含大量三角形的复杂烘焙网格表示，或对烘焙表示进行资源密集型的射线步进。我们挑战这些传统做法，观察到以高三角形网格为代表的高质量几何并非实现逼真渲染质量的必要条件。为此，我们提出MixRT——一种新颖的NeRF表示方法，包含低质量网格、视角相关位移图与压缩NeRF模型。该设计有效利用了现有图形硬件的计算能力，从而在边缘设备上实现实时NeRF渲染。借助高度优化的基于WebGL的渲染框架，我们所提出的MixRT在边缘设备上（MacBook M1 Pro笔记本上以1280×720分辨率运行速度超过30 FPS）实现了实时渲染速度，更优的渲染质量（在Unbounded-360数据集室内场景中PSNR提升0.2），以及更小的存储体积（相比最先进方法节省超过20%）。