We present a novel framework to regularize Neural Radiance Field (NeRF) in a
few-shot setting with a geometry-aware consistency regularization. The proposed
approach leverages a rendered depth map at unobserved viewpoint to warp sparse
input images to the unobserved viewpoint and impose them as pseudo ground
truths to facilitate learning of NeRF. By encouraging such geometry-aware
consistency at a feature-level instead of using pixel-level reconstruction
loss, we regularize the NeRF at semantic and structural levels while allowing
for modeling view dependent radiance to account for color variations across
viewpoints. We also propose an effective method to filter out erroneous warped
solutions, along with training strategies to stabilize training during
optimization. We show that our model achieves competitive results compared to
state-of-the-art few-shot NeRF models. Project page is available at
this https URL

本文提出一种新的框架来规范化在几个样本（few-shot）设置中的神经辐射场（NeRF），通过几何感知一致性正则化。该方法利用了未观察视角的渲染深度图来将稀疏的输入图像变形到未观察视角并将其作为伪地面实现 NeRF 的学习，从而在特征层面而非像素级重建损失上鼓励这种几何感知的一致性，实现 NeRF 在语义和结构层面上的规范化，同时允许建模视角依赖性辐射以考虑不同视角的颜色变化。同时，本文提出一种有效的方法来筛除错误变形的解，以及稳定训练优化的训练策略。我们展示了本文提出的模型与现有最先进的几个样本设置下的 NeRF 模型相比具有竞争优势。