FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation
<|reference_start|>FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid Scene-Specific Adaptation: 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.<|reference_end|>
@article{sivakumar2024fewshotnerf:,
    title={FewShotNeRF: Meta-Learning-based Novel View Synthesis for Rapid
  Scene-Specific Adaptation},
    author={Piraveen Sivakumar, Paul Janson, Jathushan Rajasegaran, Thanuja
  Ambegoda},
    journal={arXiv preprint arXiv:2408.04803},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04803},
    primaryClass={cs.CV}
}