Real-Time Point Cloud Visualization for Sustainable Spatial Digital Twins

City-scale spatial digital twins require extensive spatial information, such as city-scale point clouds, to remain updated. This study develops a 3D mobile crowdsensing system in which participants collect 3D point cloud data of urban spaces using LiDAR-equipped mobile devices. This system aggregates the collected partial point clouds on a server to merge them into the city digital twin. In such environments, city-scale point clouds are expected to support various smart city tasks. Therefore, diverse types of visualization are necessary for analysis and validation, including task examination, real-time system behavior verification, and crowdsensing system tracking. In this paper, we present a web-based, real-time point cloud visualization mechanism designed to offer tailored views for different use cases.