Automating the Creation of As-built Building Information Models

该奖项由2009年美国复苏和再投资法案(公共法律111-5)资助。建筑信息模型(BIM)代表了设施的三维(3D)几何和高级语义，越来越多地应用于建筑、工程、建筑和设施管理(AEC/FM)行业。大多数BIM工作侧重于表示设施的设计状况，但由于施工或翻新过程中的变化，实际建成或使用状况可能与设计有很大不同。目前，竣工BIM的使用受到限制，因为它们很难创建且耗时，而且现有的BIM标准不完全支持表示竣工条件。这项研究将解决这些障碍，方法是开发算法，根据激光扫描仪收集的点云数据自动创建竣工模型，并开发新的表示法来支持BIM利益相关者的需求。建模目标将集中在点到BIM转换过程的三个方面：几何建模，其中原始点被分割成几何组件，例如平面区域，并以参数形式(例如，平面参数和边界)进行建模；语义标记，其中被建模的组件被分配有意义的标签，例如“墙”或“天花板”；以及遮挡推理，其中基于可见表面的几何形状来估计未可视化的表面。表示目标将集中在表示建成BIM问题的两个方面：细节层次解决了处理建成模型中固有的大型3D点集的困难。表示将被形式化，以支持多个详细级别，这将支持高效的高级别分析，同时支持深入到原始数据点级别的详细分析。元数据表示的目标是开发关于如何从原始数据中获取信息以及如何收集原始数据的描述。方法将被形式化以支持二次数据的表示，例如偏离理想化模型、由于遮挡而丢失数据以及传感器配置和放置。总而言之，这些目标包括一种端到端的方法，以简化点到BIM转换流程，并可能改变当前使用/利用BIM和3D成像技术的方式。对这些方法的评估将使用我们团队和我们的合作者在案例研究中使用的不同类型扫描仪的激光扫描数据进行。这项研究有望改变BIM的创建和使用方式。本研究开发的算法和表示策略旨在极大地简化竣工BIM的创建过程，并将为在施工和设施管理过程中分析和使用BIM创造新的机会。这项研究的逆向工程方面还将推进通用3D场景解释的一般领域，对不同领域产生影响，包括机器人(例如，为室内移动机器人创建建筑模型)、建筑安全(例如，为第一反应人员自动绘制建筑地图)和建筑工地监控。这项研究将被纳入现有的卡内基梅隆大学的课程，以及一个新的关于建成的BIM的项目课程，由私人投资机构共同教授。该项目的视觉特性有助于将其纳入该团队参与的K-12和少数族裔外联方案。我们计划通过互联网提供这项研究的产品，包括数据集和软件，这是有益的，因为这种类型的3D数据集并不普遍可用，创建起来困难/昂贵。

This award is funded under the American Recovery and Reinvestment Act of 2009 (public Law 111-5).Building information models (BIMs), which represent the three dimensional (3D) geometry and high-level semantics of a facility, are increasingly used in the Architecture, Engineering, Construction, and Facility Management (AEC/FM) industry. Most BIM work focuses on representing the as-designed conditions of a facility, but the actual as-built or as-used conditions can differ significantly from the design due to changes during construction or renovations. Currently, the utilization of as-built BIMs is limited because they are difficult and time-consuming to create and because existing BIM standards do not fully support representing as-built conditions. This research will address these barriers by developing algorithms to automate the creation of as-built models from point cloud data collected using laser scanners and by developing new representations that support the needs of BIM stakeholders. The modeling objective will focus on three aspects of the points-to-BIM transformation process: Geometric modeling, in which raw points are segmented into geometric components, such as planar regions, and modeled parametrically (e.g., plane parameters and boundaries); Semantic labeling, in which modeled components are assigned meaningful labels, such as "wall" or "ceiling"; and occlusion inference, in which surfaces that are not visualized are estimated based on the geometry of visible surfaces. The representation objective will focus on two aspects of the problem of representing as-built BIMs: levels of detail addresses the difficulty of handling large 3D point sets inherent in as-built models. Representations will be formalized that support multiple levels of detail, which will enable efficient high-level analysis, while supporting detailed analysis down to the level of raw data points. Metadata representation targets development of descriptions of how information is derived from raw data and how raw data is collected. Approaches will be formalized to support the representation of secondary data, such as deviations from idealized models, missing data due to occlusion, and sensor configuration and placement. Taken together, these objectives comprise an end-to-end approach to streamline the points-to-BIM conversion process and it is likely that it transform the current way of using/leveraging BIM and 3D imaging technologies. Evaluation of these approaches will be conducted using laser scan data from different types of scanners used in case studies generated by our group and by our collaborators.This research is expected to transform the way that BIMs are created and utilized. The algorithms and representation strategies developed under this research are intended to drastically simplify process of creating of as-built BIMs and will create new opportunities of analyzing and utilizing BIMs during construction and facility management. The reverse engineering aspects of this research will also advance the general area of generic 3D scene interpretation, with impact in diverse domains, including robotics (e.g., creating building models for indoor mobile robots), building safety (e.g., automatic mapping of buildings for first responders), and construction site monitoring. This research will be incorporated into existing Carnegie Mellon courses as well as a new project course on as-built BIMs, to be co-taught by the PIs. The visual nature of the project lends itself to inclusion in K-12 and minority outreach programs in which the team participates. We plan to make the products of this research available via the Internet, including data sets and software, which is beneficial, since 3D data sets of this kind are not generally available and are difficult/costly to create.