一种面向建筑点云抗杂乱平面图生成的混合语义-几何方法
A Hybrid Semantic-Geometric Approach for Clutter-Resistant Floorplan Generation from Building Point Clouds
AI总结：
本文提出一种混合语义-几何方法，结合PointNet++语义分割、区域生长和双重RANSAC，从含杂乱物的建筑点云中生成抗干扰的二维平面图，并通过多项指标验证其有效性。
摘要：
建筑信息模型（BIM）技术是现代建筑工程和项目管理流程的关键组成部分。反映项目场地空间现实的现状BIM模型可为利益相关者提供重要信息，用于施工进度监控、错误检查和建筑维护。将原始扫描数据自动转换为BIM模型的几何方法（Scan-to-BIM）往往未能利用数据中更高层次的语义信息。而语义分割方法仅在点级别输出标签，无法创建BIM所需的对象级模型。为解决这些问题，本研究提出一种混合语义-几何方法，用于从激光扫描的建筑点云中生成抗杂乱的平面图。输入点云首先通过坐标系统归一化和去除离群点进行预处理。然后，使用基于PointNet++的语义分割网络将每个点标记为天花板、地板、墙壁、门、楼梯和杂乱物。杂乱点被移除，而墙壁、门和楼梯点用于二维平面图生成。应用区域生长分割算法并结合几何推理规则，将点分组为各个建筑构件。最后，应用双重随机样本一致性（RANSAC）算法将建筑构件参数化为二维线段，用于生成输出平面图。所提方法使用精确率、召回率、交并比（IOU）、Betti误差和翘曲误差等指标进行评估。
英文摘要：
Building Information Modeling (BIM) technology is a key component of modern construction engineering and project management workflows. As-is BIM models that represent the spatial reality of a project site can offer crucial information to stakeholders for construction progress monitoring, error checking, and building maintenance purposes. Geometric methods for automatically converting raw scan data into BIM models (Scan-to-BIM) often fail to make use of higher-level semantic information in the data. Whereas, semantic segmentation methods only output labels at the point level without creating object level models that is necessary for BIM. To address these issues, this research proposes a hybrid semantic-geometric approach for clutter-resistant floorplan generation from laser-scanned building point clouds. The input point clouds are first pre-processed by normalizing the coordinate system and removing outliers. Then, a semantic segmentation network based on PointNet++ is used to label each point as ceiling, floor, wall, door, stair, and clutter. The clutter points are removed whereas the wall, door, and stair points are used for 2D floorplan generation. A region-growing segmentation algorithm paired with geometric reasoning rules is applied to group the points together into individual building elements. Finally, a 2-fold Random Sample Consensus (RANSAC) algorithm is applied to parameterize the building elements into 2D lines which are used to create the output floorplan. The proposed method is evaluated using the metrics of precision, recall, Intersection-over-Union (IOU), Betti error, and warping error.
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。
