  Recent works on generalizable NeRFs have shown promising results on novel
view synthesis from single or few images. However, such models have rarely been
applied on other downstream tasks beyond synthesis such as semantic
understanding and parsing. In this paper, we propose a novel framework named
FeatureNeRF to learn generalizable NeRFs by distilling pre-trained vision
foundation models (e.g., DINO, Latent Diffusion). FeatureNeRF leverages 2D
pre-trained foundation models to 3D space via neural rendering, and then
extract deep features for 3D query points from NeRF MLPs. Consequently, it
allows to map 2D images to continuous 3D semantic feature volumes, which can be
used for various downstream tasks. We evaluate FeatureNeRF on tasks of 2D/3D
semantic keypoint transfer and 2D/3D object part segmentation. Our extensive
experiments demonstrate the effectiveness of FeatureNeRF as a generalizable 3D
semantic feature extractor. Our project page is available at
https://jianglongye.com/featurenerf/ .
