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Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration
Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration
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Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration
Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration

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Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration
Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration
Journal Article

Efficient Four-Level LOD Simplification for Single- and Multi-Mesh 3D Scenes Towards Scalable BIM/GIS/Digital Twin Integration

2026
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Overview
Efficient level-of-detail (LOD) management is crucial for handling large-scale 3D meshes in BIM, GIS, and digital twin applications. In practice, both individual models and complex multi-mesh scenes require multi-resolution representations. Yet two practical issues persist: (i) simplification rates are often fixed a priori, lacking principled guidance and yielding suboptimal fidelity–cost trade-offs; and (ii) after a scene-level target is set, workflows commonly impose a uniform rate on all models, which is ill-suited to heterogeneous geometry and produces uneven visual quality. This paper presents an automatic approach that constructs a cumulative edge collapse loss curve using a QEM (Quadric Error Metrics)-based process. Shape analysis of this curve defines four representative LOD targets, and an automated procedure then determines their corresponding simplification rates. The method is first developed for individual meshes and then extended to multi-mesh scenes, assigning model-specific rates that satisfy a prescribed scene-level reduction while maintaining visual consistency. Experiments on complex engineering datasets show higher fidelity than uniform-rate baselines, especially at high reductions. The approach provides a practical, automated framework for object- and scene-level LOD generation.