Change Detection for Constantly Maintaining Up-to-date Metaverse Maps

Metaverse has been attracting more and more attention because of its potential for various use cases. In metaverse applications, the seamless integration of digital and physical worlds is vital for synchronizing information from one world to another. One way to achieve this is to reconstruct 3D environmental maps every time, which is not feasible due to computational complexity. A cheaper alternative is to detect what objects have changed and update only the changed objects. To build the foundation of the change detection algorithm for that, in this paper, we propose a change detection method combined with object classification. Despite its simplicity, the experiment showed promising results with an object detector fine-tuned with data from the target environment. Furthermore, with our clustering-based post-processing, false positives produced by the frame-wise change detection were observed to be successfully suppressed.