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Improving Image Matching Between Digital Twin and Real-World Using Cross-Domain Geo-Localization Techniques

Year: 2025

Type: article

Abstract: Digital twins, as virtual replicas of real-world entities, have gained attention for their ability to synchronize with reality and facilitate simulations. However, inherent differences between digital twin and real-world datasets challenge traditional image-based matching methods. This paper evaluates the performance of cross-domain image geo-localization methods applied to digital twins and the real world, focusing on visual and semantic feature matching. Experimental results show that both methods perform well, with semantic feature matching outperforming visual feature matching, indicating its advantage in handling differing visual characteristics between domains. These findings confirm the potential of semantic feature matching for effective cross-domain matching between digital twins and the real world.

Source: Journal of the Korea Institute of Military Science and Technology

DOI: 10.9766/kimst.2025.28.1.037

Authors: Soorim Yang, Youngshin Kim, Kanghoon Choi, Sangyun Kim

Language: English

Topic: Advanced Image and Video Retrieval Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Korea Research Institute for Defense Technology Planning and Advancement, Defense Acquisition Program Administration

Award: G6911939493

OpenAlex ID: W4407164698