FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis
<|reference_start|>FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis: 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.<|reference_end|>
@article{xing2024framenerf:,
    title={FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View
  Synthesis},
    author={Yan Xing, Pan Wang, Ligang Liu, Daolun Li and Li Zhang},
    journal={arXiv preprint arXiv:2402.14586},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.14586},
    primaryClass={cs.CV cs.GR}
}