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.

翻译：我们提出了一种名为FrameNeRF的新型框架，旨在将现成的快速高保真NeRF模型（具有快速训练速度和高渲染质量）应用于少样本新视角合成任务。快速高保真模型的训练稳定性通常受限于密集视图，使其不适用于少样本新视角合成。为解决这一局限，我们利用一个正则化模型作为数据生成器，从稀疏输入中生成密集视图，从而促进后续快速高保真模型的训练。由于这些密集视图是正则化模型生成的伪地面真值，我们随后使用原始稀疏图像对快速高保真模型进行微调。这一过程有助于模型学习真实细节并修正早期阶段引入的伪影。通过结合现成的正则化模型与快速高保真模型，我们的方法在多个基准数据集上实现了最先进的性能。