中文标题#
CM2LoD3：使用语义冲突图重建 LoD3 建筑模型
英文标题#
CM2LoD3: Reconstructing LoD3 Building Models Using Semantic Conflict Maps
中文摘要#
详细的 3D 建筑模型对于城市规划、数字孪生和灾害管理应用至关重要。 虽然 Level of Detail 1 (LoD) 1 和 LoD2 建筑模型广泛可用，但它们缺乏用于高级城市分析的关键立面元素。 相比之下，LoD3 模型通过包含窗户、门和人行通道等立面元素来解决这一限制。 然而，其生成传统上需要手动建模，使得大规模采用具有挑战性。 在本贡献中， CM2LoD3，我们提出了一种新方法，利用从射线到模型先验分析中获得的冲突地图（CMs）来重建 LoD3 建筑模型。 与之前的工作不同，我们专注于使用我们开发的语义冲突地图生成器（SCMG）生成的合成 CMs 对现实世界的 CMs 进行语义分割。 我们还观察到，可以通过置信度分数将纹理模型的额外分割与 CMs 融合，以进一步提高分割性能，从而提高 3D 重建精度。 实验结果表明，我们的 CM2LoD3 方法在分割和重建建筑开口方面是有效的，具有 61% 的不确定性感知融合分割建筑纹理的性能。 这项研究有助于自动化 LoD3 模型重建的进步，为可扩展和高效的 3D 城市建模铺平了道路。 我们的项目可用：https://github.com/InFraHank/CM2LoD3
英文摘要#
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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