Bibliometrical analysis of 3D real scene modelling based on Web of Science

The 3D real scene models can vividly and intuitively represent various aspects of urban information. These models have been applied in multiple fields. However, a quantitative review of the development status and trends in 3D real scene modelling research is still scarce. To address this gap, this study performed a bibliometric analysis. We analysed 3701 research articles indexed in the Web of Science Core Collection from 2005 to 2024. We used bibliometric tools including Bibliometrix, COOC software, and CiteSpace to investigate the research status and hotspots in the 3D real scene modelling field. We also identified the current gaps and strengths. In addition, we discuss future research priorities and development directions. The results revealed the following: 1) Key Research Topics: The primary research focuses include these areas: point cloud, virtual reality, 3D reconstruction, cluttered scene, constraint-free view-based 3D object retrieval, neural radiance field, and textured urban model. 2) Growth Trends: Research interest in 3D real scene modelling has rapidly grown since 2019 and continues to expand. Technologies such as deep learning have played an increasingly significant role. Emerging directions, such as AI-assisted modelling and the integration of the metaverse, have recently become prominent. 3) Highly Cited Papers: Highly cited studies primarily focus on topics such as deep learning algorithms and neural radiance field. This review systematically outlines the development trajectory of 3D real scene modelling research and provides valuable references for future studies and applications.