OPTIMIZATION OF 3D DATA FORMATS AND EFFICIENCY FOR WEB-BASED SUBSURFACE VISUALIZATION USING 3D TILES

Abstract

WebGIS-based three-dimensional (3D) data visualization often faces challenges associated with large data volumes, which can affect storage efficiency and visualization performance. This study aims to optimize 3D data formats for subsurface visualization using 3D Tiles, with Barong Cave as the case study. The 3D model was reconstructed using Agisoft Metashape and subsequently processed through four workflows comparing LAS and LAZ formats and geometry optimization through subsampling. All models were converted into 3D Tiles and visualized using CesiumJS. The results demonstrate that the optimization process reduced the number of points in the point cloud by 80.72%, while the use of the LAZ format reduced file size by approximately 67% compared with LAS. Workflow 3 achieved the fastest loading time at 180 ms, while memory usage remained stable within the range of 55.83–57.17 MB. All workflows achieved frame rates above 90 FPS, enabling real-time visualization. These findings demonstrate that the combination of LAZ and 3D Tiles can improve data and visualization efficiency while maintaining the representational quality of the 3D model.