We present a novel framework, called FrameNeRF, designed to apply off-the-shelf fast high-fidelity NeRF models with fast training speed and high rendering quality for few-shot novel view synthesis tasks. The training stability of fast high-fidelity models is typically constrained to dense views, making them unsuitable for few-shot novel view synthesis tasks. To address this limitation, we utilize a regularization model as a data generator to produce dense views from sparse inputs, facilitating subsequent training of fast high-fidelity models. Since these dense views are pseudo ground truth generated by the regularization model, original sparse images are then used to fine-tune the fast high-fidelity model. This process helps the model learn realistic details and correct artifacts introduced in earlier stages. By leveraging an off-the-shelf regularization model and a fast high-fidelity model, our approach achieves state-of-the-art performance across various benchmark datasets.

我们提出了一种名为FrameNeRF的新颖框架，旨在应用现成的快速高保真NeRF模型，以实现训练速度快且渲染质量高的少样本新视角合成任务。通过使用正则化模型作为数据生成器，从稀疏输入中生成密集视角，以提供快速高保真模型的后续训练。结合现成的正则化模型和快速高保真模型，我们的方法在各种基准数据集上取得了最先进的性能。

FrameNeRF: 一个简单高效的少样本新视角综合框架