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The recent success of implicit neural scene representations has presented aviable new method for how we capture and store 3D scenes. Unlike conventional3D representations, such as point clouds, which explicitly store sceneproperties in discrete, localized units, these implicit representations encodea scene in the weights of a neural network which can be queried at anycoordinate to produce these same scene properties. Thus far, implicitrepresentations have primarily been optimized to estimate only the appearanceand/or 3D geometry information in a scene. We take the next step anddemonstrate that an existing implicit representation (SRNs) is actuallymulti-modal; it can be further leveraged to perform per-point semanticsegmentation while retaining its ability to represent appearance and geometry.To achieve this multi-modal behavior, we utilize a semi-supervised learningstrategy atop the existing pre-trained scene representation. Our method issimple, general, and only requires a few tens of labeled 2D segmentation masksin order to achieve dense 3D semantic segmentation. We explore two novelapplications for this semantically aware implicit neural scene representation:3D novel view and semantic label synthesis given only a single input RGB imageor 2D label mask, as well as 3D interpolation of appearance and semantics.

Semantic Implicit Neural Scene Representations With Semi-Supervised Training