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BIM reconstruction from 3D point clouds: A semantic registration approach based on multimodal optimization and architectural design knowledge

机译：从3D点云进行BIM重建：一种基于多峰优化和建筑设计知识的语义注册方法

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Reconstructing semantically rich building information model (BIM) from 2D images or 3D point clouds represents a research realm that is gaining increasing popularity in architecture, engineering, and construction. Researchers have found that architectural design knowledge, such as symmetry, planarity, parallelism, and orthogonality, can be utilized to improve the effectiveness of such BIM reconstruction. Following this line of enquiry, this paper aims to develop a novel semantic registration approach for complicated scenes with repetitive, irregular-shaped objects. The approach first formulates the architectural repetition as the multimodality in mathematics. Thus, the reconstruction of repetitive objects becomes a multimodal optimization (MMO) problem of registering BIM components which have accurate geometries and rich semantics. Then, the topological information about repetition and symmetry in the reconstructed BIM is recognized and regularized for BIM semantic enrichment. A university lecture hall case, consisting of 1.9 million noisy points of 293 chairs, was selected for an experiment to validate the proposed approach. Experimental results showed that a BIM was satisfactorily created (achieving about 90% precision and recall) automatically in 926.6 s; and an even more satisfactory BIM achieved 99.3% precision and 98.0% recall with detected semantic and topological information under the minimal effort of human intervention in 228.4 s. The multimodality model of repetitive objects, the repetition detection and regularization for BIM, and satisfactory reconstruction results in the presented approach can contribute to methodologies and practices in multiple disciplines related to BIM and smart city.
机译：从2D图像或3D点云重建语义丰富的建筑信息模型（BIM）代表了一个研究领域，该领域在建筑，工程和建筑中越来越受欢迎。研究人员发现，可以利用诸如对称性，平面性，平行性和正交性之类的建筑设计知识来提高这种BIM重建的有效性。根据这一询问，本文旨在为包含重复，不规则形状物体的复杂场景开发一种新颖的语义注册方法。该方法首先将建筑重复公式化为数学中的多模态。因此，重复对象的重建成为注册具有精确几何形状和丰富语义的BIM组件的多模式优化（MMO）问题。然后，识别并重构重构的BIM中有关重复性和对称性的拓扑信息，以进行BIM语义丰富化。选择了一个大学演讲厅案例，该案例由190个噪声点和293张椅子组成，用于实验以验证所提出的方法。实验结果表明，可以在926.6 s内自动令人满意地创建BIM（达到约90％的精度和召回率）；在228.4 s的人为干预下，通过检测到的语义和拓扑信息，甚至更令人满意的BIM达到了99.3％的精度和98.0％的查全率。重复对象的多模式模型，BIM的重复检测和正则化以及所提出的方法中令人满意的重建结果可以为与BIM和智慧城市相关的多个学科的方法和实践做出贡献。

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