  Neural Radiance Fields (NeRFs) encode the radiance in a scene parameterized
by the scene's plenoptic function. This is achieved by using an MLP together
with a mapping to a higher-dimensional space, and has been proven to capture
scenes with a great level of detail. Naturally, the same parameterization can
be used to encode additional properties of the scene, beyond just its radiance.
A particularly interesting property in this regard is the semantic
decomposition of the scene. We introduce a novel technique for semantic soft
decomposition of neural radiance fields (named SSDNeRF) which jointly encodes
semantic signals in combination with radiance signals of a scene. Our approach
provides a soft decomposition of the scene into semantic parts, enabling us to
correctly encode multiple semantic classes blending along the same direction --
an impossible feat for existing methods. Not only does this lead to a detailed,
3D semantic representation of the scene, but we also show that the regularizing
effects of the MLP used for encoding help to improve the semantic
representation. We show state-of-the-art segmentation and reconstruction
results on a dataset of common objects and demonstrate how the proposed
approach can be applied for high quality temporally consistent video editing
and re-compositing on a dataset of casually captured selfie videos.
