  This review thoroughly examines the role of semantically-aware Neural
Radiance Fields (NeRFs) in visual scene understanding, covering an analysis of
over 250 scholarly papers. It explores how NeRFs adeptly infer 3D
representations for both stationary and dynamic objects in a scene. This
capability is pivotal for generating high-quality new viewpoints, completing
missing scene details (inpainting), conducting comprehensive scene segmentation
(panoptic segmentation), predicting 3D bounding boxes, editing 3D scenes, and
extracting object-centric 3D models. A significant aspect of this study is the
application of semantic labels as viewpoint-invariant functions, which
effectively map spatial coordinates to a spectrum of semantic labels, thus
facilitating the recognition of distinct objects within the scene. Overall,
this survey highlights the progression and diverse applications of
semantically-aware neural radiance fields in the context of visual scene
interpretation.
