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 https://ku-cvlab.github.io/GeCoNeRF/.

翻译：摘要：我们提出了一种新颖的框架，通过几何感知一致性正则化在少样本场景中约束神经辐射场（NeRF）。所提出的方法利用在未观测视角处渲染的深度图，将稀疏输入图像扭曲至该未观测视角，并将其作为伪真实值以促进NeRF的学习。通过鼓励特征层面的几何感知一致性（而非像素级重建损失），我们在语义和结构层面对NeRF进行正则化，同时允许建模视角依赖的辐射度以解释视角间的颜色变化。我们还提出了一种有效方法过滤错误的扭曲解，并采用训练策略以稳定优化过程。实验表明，我们的模型在性能上与最先进的少样本NeRF模型相比具有竞争性。项目页面访问地址：https://ku-cvlab.github.io/GeCoNeRF/。