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).

通过提出了 MixRT，一种包含低质量网格、视角依赖位移图和压缩 NeRF 模型的新型 NeRF 表示方法，有效利用现有图形硬件的能力，在边缘设备上实现了实时 NeRF 渲染，速度快（MacBook M1 Pro 笔记本分辨率 1280 x 720 超过 30 FPS），渲染质量更好（Unbounded-360 数据集室内场景 0.2 PSNR 高），存储大小更小（与最先进的方法相比少于 80%）。