  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/.
