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.

本综述全面研究了语义感知的神经辐射场（NeRFs）在视觉场景理解中的作用，覆盖了 250 多篇学术论文的分析。它探讨了 NeRFs 在场景中熟练推断静态和动态物体的三维表示的能力，并介绍了其在生成高质量新视点、补全场景细节（修复）、进行全面场景分割（全景分割）、预测三维边界框、编辑三维场景和提取以物体为中心的三维模型方面的重要性。本研究的一个重要方面是将语义标签应用为视点不变函数，有效地将空间坐标映射到一系列语义标签的过程，从而便于识别场景中的不同物体。总体而言，本综述强调了在视觉场景解释的背景下，语义感知的神经辐射场的发展和多样应用。