  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.
