Efficient visualization of 3D city scenes by integrating the GIS and Unreal Engine

Hai Xu; Biao He; ZeYu Li; HaoJia Lin; AoWei Tang

doi:10.1117/12.2668104

23 February 2023 Efficient visualization of 3D city scenes by integrating the GIS and Unreal Engine

Hai Xu, Biao He, ZeYu Li, HaoJia Lin, AoWei Tang

Proceedings Volume 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022); 125510I (2023) https://doi.org/10.1117/12.2668104
Event: Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 2022, Changchun, China

Abstract

Achieving a smooth and high-fidelity visualization of 3D city scenes in virtual geographic environment is a key technology for digital twins. Geographical information system (GIS) researchers have proposed many methods for loading and visualizing such city-scale scenes. However, if the scale of the scene is too large, the current methods cannot achieve a smooth and high-fidelity visualization of 3D city scenes. In this study, we propose an efficient visualization method for 3D city scenes through calculating potential visible sets (PVS) and hierarchical levels of detail (HLOD). We use adaptive quadtree partition to divide the 3D city scenes. Then, we calculate the potential visible set of each leaf node using an improved ray-casting algorithm, and visual perception parameters model were evaluated by employing the screen error, which was used to constrain the dynamic scheduling of HLOD. We implemented our method on Unreal Engine (UE4) and designed multiple sets of controlled experiments to illustrate the advantages of our method in terms of visualization efficiency and rendering effect. The results show that our method can significantly improve the visualization fluency, maintain good global and local detail.

Citation Download Citation

Hai Xu, Biao He, ZeYu Li, HaoJia Lin, and AoWei Tang "Efficient visualization of 3D city scenes by integrating the GIS and Unreal Engine", Proc. SPIE 12551, Fourth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2022), 125510I (23 February 2023); https://doi.org/10.1117/12.2668104

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