  We present a novel approach to perform 3D semantic segmentation solely from
2D supervision by leveraging Neural Radiance Fields (NeRFs). By extracting
features along a surface point cloud, we achieve a compact representation of
the scene which is sample-efficient and conducive to 3D reasoning. Learning
this feature space in an unsupervised manner via masked autoencoding enables
few-shot segmentation. Our method is agnostic to the scene parameterization,
working on scenes fit with any type of NeRF.
