A Method of Crowdsourced Task Request Optimization for 3D Reconstruction in Urban Space

This paper proposes an optimization method for scanning task requests to reduce various costs involved in creating and updating 3D models. In recent years, there has been a growing interest in research related to metaverses. Among them, the utilization of 3D models created by scanning real-world buildings has various applications. Examples include virtual travel experiences and simulations for autonomous driving. However, the creation of 3D models is time-consuming and costly, As a result, the updating of 3D models occurs on a time scale of several months to several years. However, due to the low frequency of updating 3D models, virtual travel experiences can be unsatisfying compared to real-world travel. For example, objects related to seasonal events or foliage changes in trees may not be updated, resulting in a lack of immersion in the virtual travel experience. In this study, we propose a method to induce (request) general participants interested in the target object to take photos that are useful for 3D model creation and updating. The goal of this research is to increase the frequency of creating and updating 3D models, thereby achieving a more realistic metaverse experience.