用冲突图自动重建带门窗的3D建筑模型，提升城市建模效率。
CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
- 基于射线-模型先验生成冲突图，结合语义分割实现自动重建。
- 融合纹理分割结果与置信度，61%准确率提升开口识别精度。
- 适合城市规划、数字孪生领域，推动大规模3D城市场景构建。
详细的3D建筑模型对城市规划、数字孪生和灾害管理至关重要。尽管LoD1和LoD2模型广泛可用，但缺乏窗户、门、通道等立面细节，难以支持高级城市分析。相比之下，LoD3模型通过包含这些元素弥补了缺陷，但传统生成依赖人工建模，难以规模化。本文提出CM2LoD3方法，利用射线-模型先验分析获得的冲突图（CMs）进行LoD3模型重建。不同于以往工作，我们聚焦于真实冲突图的语义分割，结合自研语义冲突图生成器（SCMG）生成的合成冲突图。同时发现，将纹理模型的额外分割结果与冲突图通过置信度融合，可进一步提升分割性能，从而提高3D重建精度。实验表明，该方法在建筑开口分割与重建中有效，不确定性感知融合下达到61%的性能表现。本研究推进了自动化LoD3模型重建，为可扩展、高效的3D城市建模铺平道路。项目开源：https://github.com/InFraHank/CM2LoD3。
原文摘要 · Abstract (English)
Detailed 3D building models are crucial for urban planning, digital twins, and disaster management applications. While Level of Detail 1 (LoD)1 and LoD2 building models are widely available, they lack detailed facade elements essential for advanced urban analysis. In contrast, LoD3 models address this limitation by incorporating facade elements such as windows, doors, and underpasses. However, their generation has traditionally required manual modeling, making large-scale adoption challenging. In this contribution, CM2LoD3, we present a novel method for reconstructing LoD3 building models leveraging Conflict Maps (CMs) obtained from ray-to-model-prior analysis. Unlike previous works, we concentrate on semantically segmenting real-world CMs with synthetically generated CMs from our developed Semantic Conflict Map Generator (SCMG). We also observe that additional segmentation of textured models can be fused with CMs using confidence scores to further increase segmentation performance and thus increase 3D reconstruction accuracy. Experimental results demonstrate the effectiveness of our CM2LoD3 method in segmenting and reconstructing building openings, with the 61% performance with uncertainty-aware fusion of segmented building textures. This research contributes to the advancement of automated LoD3 model reconstruction, paving the way for scalable and efficient 3D city modeling. Our project is available: https://github.com/InFraHank/CM2LoD3
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