MLS2LoD3：利用MLS点云细化低细节层次建筑模型以重建语义化LoD3建筑模型
MLS2LoD3: Refining low LoDs building models with MLS point clouds to reconstruct semantic LoD3 building models
AI总结：
本文提出一种利用MLS点云细化低LoD建筑模型以重建语义化LoD3模型的新策略，并给出立面元素重建及CityGML嵌入指南，旨在促进LoD3重建算法的开发与广泛应用。
摘要：
尽管高细节层次的LoD3建筑模型在各种应用中展现出巨大潜力，但它们尚未普及。创建此类模型的主要挑战不仅涉及自动检测与重建，还涉及符合标准的建模。在本文中，我们提出了一种新颖的细化策略，通过利用较低LoD建筑模型的普遍性和MLS点云的准确性来实现LoD3重建。该策略有望实现大规模LoD3重建，并解锁LoD3应用，我们也在本文中对此进行了描述和说明。此外，我们提出了重建LoD3立面元素并将其嵌入CityGML标准模型的指南，将所获得的知识传播给学术界和专业人士。我们相信，我们的方法能够促进LoD3重建算法的发展，并进而推动其更广泛的采用。
英文摘要：
Although highly-detailed LoD3 building models reveal great potential in various applications, they have yet to be available. The primary challenges in creating such models concern not only automatic detection and reconstruction but also standard-consistent modeling. In this paper, we introduce a novel refinement strategy enabling LoD3 reconstruction by leveraging the ubiquity of lower LoD building models and the accuracy of MLS point clouds. Such a strategy promises at-scale LoD3 reconstruction and unlocks LoD3 applications, which we also describe and illustrate in this paper. Additionally, we present guidelines for reconstructing LoD3 facade elements and their embedding into the CityGML standard model, disseminating gained knowledge to academics and professionals. We believe that our method can foster development of LoD3 reconstruction algorithms and subsequently enable their wider adoption.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
