Neural radiance fields (NeRFs) are a widely accepted standard for
synthesizing new 3D object views from a small number of base images. However,
NeRFs have limited generalization properties, which means that we need to use
significant computational resources to train individual architectures for each
item we want to represent. To address this issue, we propose a few-shot
learning approach based on the hypernetwork paradigm that does not require
gradient optimization during inference. The hypernetwork gathers information
from the training data and generates an update for universal weights. As a
result, we have developed an efficient method for generating a high-quality 3D
object representation from a small number of images in a single step. This has
been confirmed by direct comparison with the state-of-the-art solutions and a
comprehensive ablation study.

提出了一种基于超网络范例的少样本学习方法，通过从训练数据中收集信息并生成通用权重的更新，实现了从少量图像中一步生成高质量的 3D 物体表示的高效方法。通过与最先进的解决方案进行直接比较以及全面的剔除研究，证实了该方法的有效性。