Semantic 3D Gaussian Splatting: A State-of-the-Art Review

3D Gaussian splatting (3DGS) has recently emerged as an efficient and scalable method for high-fidelity 3D scene reconstruction, representation, and real-time rendering. In addition to geometric reconstruction, increasing research attention focuses on enriching 3D Gaussian primitives with semantic information, which can be related to an arbitrary application or domain, as well as common knowledge. However, the existing approaches to semantic 3DGS significantly differ in how semantics are represented, learned, and accessed, which makes systematic analysis difficult. This paper provides a review on semantic extensions to 3DGS. We introduce a unified multi-axis taxonomy that enables us to classify the available methods in terms of five complementary categories: semantic vocabulary space, representation form, functional role, knowledge source, and query mechanism. The analysis reveals key design trade-offs related to the flexibility, efficiency, and semantic expressiveness of the methods. Furthermore, we review datasets, benchmarks, and evaluation metrics used in the field, indicating the diversity of approaches and the lack of common evaluation frameworks. Based on this analysis, we also identify open challenges and possible future research directions. The presented survey is relevant to advances in games and immersive technologies, where semantically enriched real-time 3D representations are essential for interactive environments, AR/VR, and intelligent scene understanding. The systematic analysis presented in this survey aims to facilitate a deeper understanding of semantic 3DGS and support the development of more general, efficient, and task-aware 3D scene understanding systems.