Super-Resolution Neural Radiance Field via Learning High Frequency Details for High-Fidelity Novel View Synthesis

While neural rendering approaches facilitate photo-realistic rendering in novel view synthesis tasks, the challenge of high-resolution rendering persists due to the substantial costs associated with acquiring and training data. Recently, several studies have been proposed that render high-resolution scenes by either super-sampling points or using reference images, aiming to restore details in low-resolution (LR) images. However, super-sampling is computationally expensive, and methods with reference images require high-resolution (HR) images for inference. In this letter, we propose a novel super-resolution (SR) neural radiance field (NeRF) framework for high-fidelity novel view synthesis. To address the representation of high-fidelity HR images from the captured LR images, we learn a mapping function that maps LR rendering images to the Fourier space to restore insufficient high frequency details and render HR images at higher resolution. Experiments demonstrate that our results are quantitatively and qualitatively better than those of the existing SR methods in novel view synthesis. By visualizing the estimated dominant frequency components, we provide visual interpretations of the performance improvement.