通过渐进式傅里叶频率控制，实现少样本下快速高质量三维重建。
FourieRF: Few-Shot NeRFs via Progressive Fourier Frequency Control
- 渐进增加场景复杂度，显式控制傅里叶频率以优化特征参数化
- 在多种场景中表现鲁棒，显著减少重建伪影
- 适合少样本三维渲染任务，尤其适用于需要快速重建的场景
本文提出FourieRF，一种在少样本条件下实现快速且高质量重建的新方法。该方法通过显式的课程训练过程对特征进行参数化，逐步增加优化过程中的场景复杂度。实验表明，该方法引入的先验在多种场景下均表现出强鲁棒性与适应性，为少样本渲染问题建立了强大且通用的基线。尽管其显著降低了伪影，但在极端欠约束情况下（如视图遮挡导致部分形状未被覆盖）仍可能出现重建误差。未来可通过融合基础模型，利用大规模数据驱动先验来补全缺失区域。
原文摘要 · Abstract (English)
In this work, we introduce FourieRF, a novel approach for achieving fast and high-quality reconstruction in the few-shot setting. Our method effectively parameterizes features through an explicit curriculum training procedure, incrementally increasing scene complexity during optimization. Experimental results show that the prior induced by our approach is both robust and adaptable across a wide variety of scenes, establishing FourieRF as a strong and versatile baseline for the few-shot rendering problem. While our approach significantly reduces artifacts, it may still lead to reconstruction errors in severely under-constrained scenarios, particularly where view occlusion leaves parts of the shape uncovered. In the future, our method could be enhanced by integrating foundation models to complete missing parts using large data-driven priors.
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