Abstract
There are various structures in the underground space, and the structures are managed as 3D data in underground geospatial information map system (UGIMS). However, there is a problem that takes a long time to load the data into a mobile app from UGIMS because the data are very large. In order to reduce the loading time of 3D data, the number of data vertices should be reduced within a range that can keep the shape of 3D object in a mobile app. In this paper, we try to reduce the number of vertices using a 3D Mesh Simplification method to reduce the loading time of data in a mobile app. The proposed method applies simplification by extracting refined vertex feature information using a deep learning encoder‐decoder based model and performing vertex clustering by grouping similar vertices together with the feature information using K‐means clustering. We were able to reduce the number of vertices by 30% compared with the previous data. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
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