ABSTRACT. The objective of this paper is to systematically review spatial cognition and situational awareness algorithms, hyper-realistic artificial intelligence-generated faces, and semantic network representations in the industrial metaverse. The findings and analyses highlight that ambient sound recognition software, biometrics data fusion, and blockchain token-based digital assets are pivotal in the interconnected metaverse. Throughout January 2023, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “the industrial metaverse” + “3D computer vision-based production and simulation modeling technologies,” “geolocation data mining and tracking,” and “cognitive mapping and navigation tools.” As research published between 2022 and 2023 was inspected, only 143 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 26 mainly empirical sources. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: AMSTAR, Dedoose, Distiller SR, and SRDR.
JEL codes: D53; E22; E32; E44; G01; G41

Keywords: 3D computer vision; production and simulation modeling technologies; geolocation data mining and tracking; cognitive mapping and navigation tools; industrial metaverse

How to cite: Belas, J., Palcak, L., and Lăzăroiu, G. (2023). “3D Computer Vision-based Production and Simulation Modeling Technologies, Geolocation Data Mining and Tracking, and Cognitive Mapping and Navigation Tools in the Industrial Metaverse,” Economics, Management, and Financial Markets 18(1): 9–24. doi: 10.22381/emfm18120231.

Received 18 January 2023 • Received in revised form 25 March 2023
Accepted 28 March 2023 • Available online 30 March 2023