  Traditional Radiance Field (RF) representations capture details of a specific
scene and must be trained afresh on each scene. Semantic feature fields have
been added to RFs to facilitate several segmentation tasks. Generalised RF
representations learn the principles of view interpolation. A generalised RF
can render new views of an unknown and untrained scene, given a few views. We
present a way to distil feature fields into the generalised GNT representation.
Our GSN representation generates new views of unseen scenes on the fly along
with consistent, per-pixel semantic features. This enables multi-view
segmentation of arbitrary new scenes. We show different semantic features being
distilled into generalised RFs. Our multi-view segmentation results are on par
with methods that use traditional RFs. GSN closes the gap between standard and
generalisable RF methods significantly. Project Page:
https://vinayak-vg.github.io/GSN/
