Appearance-driven conversion of polygon soup building models with level of detail control for 3D geospatial applications

• Main Authors: Chen, Kan, Johan, Henry, Erdt, Marius
• Other Authors: School of Computer Science and Engineering
• Format: Article
• Language: English
• Published: 2020
• Subjects: Engineering::Computer science and engineering; 3D Geospatial; Model Conversion
• Online Access: https://hdl.handle.net/10356/138209
• Institution: Nanyang Technological University

In many 3D applications, building models in polygon-soup representation are commonly used for the purposes of visualization, for example, in movies and games. Their appearances are fine, however geometry-wise, they may have limited information of connectivity and may have internal intersections between their parts. Therefore, they are not well-suited to be directly used in 3D geospatial applications, which usually require geometric analysis. For an input building model in polygon-soup representation, we propose a novel appearance-driven approach to interactively convert it to a two-manifold model, which is more well-suited for 3D geospatial applications. In addition, the level of detail (LOD) can be controlled interactively during the conversion. Because a model in polygon-soup representation is not well-suited for geometric analysis, the main idea of the proposed method is extracting the visual appearance of the input building model and utilizing it to facilitate the conversion and LODs generation. The silhouettes are extracted and used to identify the features of the building. After this, according to the locations of these features, horizontal cross-sections are generated. We then connect two adjacent horizontal cross-sections to reconstruct the building. We control the LOD by processing the features on the silhouettes and horizontal cross-sections using a 2D approach. We also propose facilitating the conversion and LOD control by integrating a variety of rasterization methods. The results of our experiments demonstrate the effectiveness of our method.