FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916137893101568 |
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| author | Xing, Yan Wang, Pan Liu, Ligang Li, Daolun Zhang, Li |
| author_facet | Xing, Yan Wang, Pan Liu, Ligang Li, Daolun Zhang, Li |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14586 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis Xing, Yan Wang, Pan Liu, Ligang Li, Daolun Zhang, Li Computer Vision and Pattern Recognition Graphics 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. |
| title | FrameNeRF: A Simple and Efficient Framework for Few-shot Novel View Synthesis |
| topic | Computer Vision and Pattern Recognition Graphics |
| url | https://arxiv.org/abs/2402.14586 |