Structure- and Semantics-Aware Mesh Simplification for Generating Lightweight 3D Building Models

Abstract

Highlights: What are the main findings? We propose a dual-constraint edge-collapse simplification method, jointly enforcing structural constraints and semantic constraints to generate lightweight 3D building models while maintaining high geometric accuracy and semantic consistency. Experiments on Sketchfab, ArCH, STPLS3D and SUM datasets demonstrate the superiority of the proposed method in preserving key structural features of building models under high compression ratios compared to existing methods. What are the implications of the main findings? We provide an effective approach for building model simplification, which is particularly useful for mitigating fine-scale structural degradation and semantic discontinuities commonly observed in conventional methods. The generated lightweight building models are enriched with both geometric details and semantic context, supporting CityGML-compliant 3D model storage, intelligent management, and downstream analysis in digital twin applications. Achieving lightweight representations of building mesh models with accurate geometry and fine structural details is a key challenge in urban 3D modelling. Most existing mesh simplification methods focus on minimizing geometric error while neglecting the specific characteristics of building models in terms of geometric structure and semantic hierarchy, thus leading to structural degradation and semantic inconsistencies. To address this issue, this paper proposes a structure–semantic dual-constrained edge-collapse decimation method for simplifying dense building mesh models reconstructed from point clouds. Our core innovation lies in the joint enforcement of geometric structural constraints and building semantic constraints to effectively preserve both geometric structural features and component-level semantic structures of the models. By incorporating these two constraints, we adaptively assign higher collapse penalties to key structural edges and semantic boundaries, achieving lightweight building model simplification while maintaining fine-level structural details even under high compression ratios. Our method is extensively validated on several datasets of varying scales and complexities, including single-building models from Sketchfab, the large-scale urban datasets SUM and STPLS3D, and the ArCH cultural heritage dataset. Experimental results demonstrate that our method achieves superior or comparable performance compared to the existing methods across all the test datasets, consistently achieving lower or on-par geometric errors measured by RMSE and MAE. Furthermore, our simplified results can be semantically organized and stored under the CityGML paradigm, which provides a unified data support for sharing, semantic retrieval, downstream analysis, and other applications of lightweight building models.