FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis

• URL: http://arxiv.org/abs/2402.14586v2
• Date: Mon, 26 Feb 2024 08:13:30 GMT
• Title: FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis
• Authors: Yan Xing, Pan Wang, Ligang Liu, Daolun Li and Li Zhang
• Abstract summary: FrameNeRF is 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.
• Score: 25.356376402671536
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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