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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.04803 |
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| _version_ | 1866916350788632576 |
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| author | Sivakumar, Piraveen Janson, Paul Rajasegaran, Jathushan Ambegoda, Thanuja |
| author_facet | Sivakumar, Piraveen Janson, Paul Rajasegaran, Jathushan Ambegoda, Thanuja |
| contents | In this paper, we address the challenge of generating novel views of real-world objects with limited multi-view images through our proposed approach, FewShotNeRF. Our method utilizes meta-learning to acquire optimal initialization, facilitating rapid adaptation of a Neural Radiance Field (NeRF) to specific scenes. The focus of our meta-learning process is on capturing shared geometry and textures within a category, embedded in the weight initialization. This approach expedites the learning process of NeRFs and leverages recent advancements in positional encodings to reduce the time required for fitting a NeRF to a scene, thereby accelerating the inner loop optimization of meta-learning. Notably, our method enables meta-learning on a large number of 3D scenes to establish a robust 3D prior for various categories. Through extensive evaluations on the Common Objects in 3D open source dataset, we empirically demonstrate the efficacy and potential of meta-learning in generating high-quality novel views of objects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_04803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation Sivakumar, Piraveen Janson, Paul Rajasegaran, Jathushan Ambegoda, Thanuja Computer Vision and Pattern Recognition In this paper, we address the challenge of generating novel views of real-world objects with limited multi-view images through our proposed approach, FewShotNeRF. Our method utilizes meta-learning to acquire optimal initialization, facilitating rapid adaptation of a Neural Radiance Field (NeRF) to specific scenes. The focus of our meta-learning process is on capturing shared geometry and textures within a category, embedded in the weight initialization. This approach expedites the learning process of NeRFs and leverages recent advancements in positional encodings to reduce the time required for fitting a NeRF to a scene, thereby accelerating the inner loop optimization of meta-learning. Notably, our method enables meta-learning on a large number of 3D scenes to establish a robust 3D prior for various categories. Through extensive evaluations on the Common Objects in 3D open source dataset, we empirically demonstrate the efficacy and potential of meta-learning in generating high-quality novel views of objects. |
| title | FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2408.04803 |