  Recent advances in Neural Radiance Fields (NeRF) boast impressive
performances for generative tasks such as novel view synthesis and 3D
reconstruction. Methods based on neural radiance fields are able to represent
the 3D world implicitly by relying exclusively on posed images. Yet, they have
seldom been explored in the realm of discriminative tasks such as 3D part
segmentation. In this work, we attempt to bridge that gap by proposing SegNeRF:
a neural field representation that integrates a semantic field along with the
usual radiance field. SegNeRF inherits from previous works the ability to
perform novel view synthesis and 3D reconstruction, and enables 3D part
segmentation from a few images. Our extensive experiments on PartNet show that
SegNeRF is capable of simultaneously predicting geometry, appearance, and
semantic information from posed images, even for unseen objects. The predicted
semantic fields allow SegNeRF to achieve an average mIoU of $\textbf{30.30%}$
for 2D novel view segmentation, and $\textbf{37.46%}$ for 3D part segmentation,
boasting competitive performance against point-based methods by using only a
few posed images. Additionally, SegNeRF is able to generate an explicit 3D
model from a single image of an object taken in the wild, with its
corresponding part segmentation.
