Recently, neural radiance field (NeRF) has shown remarkable performance in
novel view synthesis and 3D reconstruction. However, it still requires abundant
high-quality images, limiting its applicability in real-world scenarios. To
overcome this limitation, recent works have focused on training NeRF only with
sparse viewpoints by giving additional regularizations, often called few-shot
NeRF. We observe that due to the under-constrained nature of the task, solely
using additional regularization is not enough to prevent the model from
overfitting to sparse viewpoints. In this paper, we propose a novel framework,
dubbed Self-Evolving Neural Radiance Fields (SE-NeRF), that applies a
self-training framework to NeRF to address these problems. We formulate
few-shot NeRF into a teacher-student framework to guide the network to learn a
more robust representation of the scene by training the student with additional
pseudo labels generated from the teacher. By distilling ray-level pseudo labels
using distinct distillation schemes for reliable and unreliable rays obtained
with our novel reliability estimation method, we enable NeRF to learn a more
accurate and robust geometry of the 3D scene. We show and evaluate that
applying our self-training framework to existing models improves the quality of
the rendered images and achieves state-of-the-art performance in multiple
settings.

提出了一种名为 SE-NeRF 的自我进化神经辐射场框架，通过在教师 - 学生框架中将 few-shot NeRF 转化为，将由教师生成的附加伪标签训练学生，通过使用可靠性估计方法提取射线级伪标签，使得 NeRF 能够学习更准确、更强健的三维场景几何，并展示与评估了将该自我训练框架应用于现有模型时改进了渲染图像的质量并在多种环境中实现了最先进的性能。