Super-resolution (SR) techniques have recently been proposed to upscale the outputs of neural radiance fields (NeRF) and generate high-quality images with enhanced inference speeds. However, existing NeRF+SR methods increase training overhead by using extra input features, loss functions, and/or expensive training procedures such as knowledge distillation. In this paper, we aim to leverage SR for efficiency gains without costly training or architectural changes. Specifically, we build a simple NeRF+SR pipeline that directly combines existing modules, and we propose a lightweight augmentation technique, random patch sampling, for training. Compared to existing NeRF+SR methods, our pipeline mitigates the SR computing overhead and can be trained up to 23x faster, making it feasible to run on consumer devices such as the Apple MacBook. Experiments show our pipeline can upscale NeRF outputs by 2-4x while maintaining high quality, increasing inference speeds by up to 18x on an NVIDIA V100 GPU and 12.8x on an M1 Pro chip. We conclude that SR can be a simple but effective technique for improving the efficiency of NeRF models for consumer devices.

翻译：超分辨率（SR）技术近期被提出用于缩放神经辐射场（NeRF）的输出，通过增强推理速度生成高质量图像。然而，现有NeRF+SR方法通过使用额外输入特征、损失函数和/或昂贵的训练流程（如知识蒸馏）增加了训练开销。本文旨在利用SR提升效率，而无需昂贵的训练或架构更改。具体而言，我们构建了一个直接结合现有模块的简单NeRF+SR流水线，并提出了一种轻量级增强技术——随机块采样用于训练。与现有NeRF+SR方法相比，我们的流水线降低了SR计算开销，训练速度可提升23倍，使其能够在Apple MacBook等消费级设备上运行。实验表明，该流水线可将NeRF输出缩放2-4倍，同时保持高质量，在NVIDIA V100 GPU和M1 Pro芯片上的推理速度分别提升18倍和12.8倍。我们得出结论：SR可作为一种简单但有效的技术，用于提升消费级设备上NeRF模型的效率。