Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: a Review of Related Techniques
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Automation in Construction 19 (2010) 829–843 Contents lists available at ScienceDirect Automation in Construction journal homepage: www.elsevier.com/locate/autcon Review Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques Pingbo Tang a, Daniel Huber b,⁎, Burcu Akinci c, Robert Lipman d, Alan Lytle e a Western Michigan University, Civil and Construction Engineering Department, Kalamazoo, MI 49008, United States b Carnegie Mellon University, Robotics Institute, 4105 Newell-Simon Hall, Pittsburgh, PA 15213, United States c Carnegie Mellon University, Civil and Environmental Engineering Department, Porter Hall 123K, Pittsburgh, PA 15213, United States d National Institute of Standards and Technology, 100 Bureau Drive, Stop 8630, Gaithersburg, MD 20899, United States e National Institute of Standards and Technology, 100 Bureau Drive, Stop 8611, Gaithersburg, MD 20899, United States article info abstract Article history: Building information models (BIMs) are maturing as a new paradigm for storing and exchanging knowledge Accepted 7 June 2010 about a facility. BIMs constructed from a CAD model do not generally capture details of a facility as it was actually built. Laser scanners can be used to capture dense 3D measurements of a facility's as-built condition Keywords: and the resulting point cloud can be manually processed to create an as-built BIM — a time-consuming, Building information models subjective, and error-prone process that could benefit significantly from automation. This article surveys Building reconstruction techniques developed in civil engineering and computer science that can be utilized to automate the process Laser scanners of creating as-built BIMs. We sub-divide the overall process into three core operations: geometric modeling, Object recognition Geometric modeling object recognition, and object relationship modeling. We survey the state-of-the-art methods for each Relationship modeling operation and discuss their potential application to automated as-built BIM creation. We also outline the Shape representation main methods used by these algorithms for representing knowledge about shape, identity, and relationships. In addition, we formalize the possible variations of the overall as-built BIM creation problem and outline performance evaluation measures for comparing as-built BIM creation algorithms and tracking progress of the field. Finally, we identify and discuss technology gaps that need to be addressed in future research. © 2010 Elsevier B.V. All rights reserved. Contents 1. Introduction .............................................................. 830 1.1. Creating as-built BIMs ...................................................... 830 1.1.1. Data collection ..................................................... 830 1.1.2. Data preprocessing ................................................... 830 1.1.3. Modeling the BIM.................................................... 830 1.2. Manual creation of as-built BIMs ................................................. 831 1.3. Automating the creation of as-built BIMs ............................................. 832 2. Knowledge representation ....................................................... 833 2.1. Shape representation ...................................................... 833 2.1.1. Explicit shape representations .............................................. 833 2.1.2. Implicit shape representations .............................................. 834 2.2. Relationship representation ................................................... 835 3. Geometric modeling .......................................................... 835 3.1. Modeling surfaces ........................................................ 835 3.1.1. Modeling planar surfaces ................................................ 835 3.1.2. Modeling curved surfaces ................................................ 835 3.1.3. Modeling extrusions .................................................. 835 3.2. Modeling volumes........................................................ 836 3.3. Modeling complex structures — windows and doors........................................ 836 ⁎ Corresponding author. Tel.: +1 412 268 2991. E-mail addresses: [email protected] (P. Tang), [email protected] (D. Huber), [email protected] (B. Akinci), [email protected] (R. Lipman), [email protected] (A. Lytle). 0926-5805/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.autcon.2010.06.007 830 P. Tang et al. / Automation in Construction 19 (2010) 829–843 4. Object recognition ........................................................... 836 4.1. Recognizing object instances ................................................... 837 4.2. Recognizing object classes .................................................... 837 4.3. Recognition using context .................................................... 838 4.4. Using prior knowledge ...................................................... 838 5. Relationship modeling ......................................................... 838 6. Performance evaluation ......................................................... 838 7. Discussion of technology gaps ...................................................... 840 8. Conclusions .............................................................. 841 Acknowledgments .............................................................. 841 References ................................................................. 841 1. Introduction creating an as-built BIM using laser scanners can be divided into three main steps: 1) data collection, in which dense point measurements of Semantically rich digital facility models, known as building the facility are collected using laser scans taken from key locations information models (BIMs), are gaining attention in the Architecture, throughout the facility; 2) data preprocessing, in which the sets of point Construction, Engineering, and Facility Management (AEC/FM) com- measurements (known as point clouds) from the collected scans are munity due to their ability to enhance communication between the filtered to remove artifacts and combined into a single point cloud or various stakeholders involved in the different stages of a facility's life surface representation in a common coordinate system; and 3) cycle [34] and their multitude of potential uses, ranging from improved modeling the BIM, in which the low-level point cloud or surface planning for renovations to more accurate modeling of a building's representation is transformed into a semantically rich BIM. These steps energy consumption [35]. A BIM represents a facility in a semantically are described in more detail in the next sub-sections. The focus of this rich manner, unlike a traditional CAD model. Whereas a CAD model article is on the modeling step, but a brief overview of the first two would represent a wall as a set of independent planar surfaces, a BIM steps is provided for completeness. would represent the wall as a single, volumetric object with multiple surfaces, as well as adjacency relationships between the wall and other 1.1.1. Data collection entities in the model, the identification of the object as a wall, and other Laser scanning technology has made the creation of as-built BIMs relevant properties (material characteristics, cost, etc.). A BIM can be tractable. Other methods, such as using a total-station, measuring tapes, used, for example, to plan the modification of a process plant with a level or even ordinary cameras, are too time-consuming or inaccurate to be of precision that would not be possible otherwise, reducing the practical on a large scale, though recent results using images are possibility of unexpected and costly delays during the construction. promising [29]. Laser scanners measure the distance from the sensor to While it is possible to construct a BIM from a CAD-based model of a nearby surfaces with millimeter to centimeter accuracy at speeds of facility's design (as-designed condition), such a model does not generally thousands to hundreds of thousands of point measurements per second capture detailed depictions of the state of a facility as it was actually built (Fig. 1). A single scan may contain several million 3D points. Since no (as-built condition) or as it exists currently (as-is condition). A facility single scanning location can visualize all surfaces within a facility, scans may not be constructed exactly as the design specified, changes may be must be obtained from multiple locations. Often, a digital camera is used made during subsequent renovations, or a design model of an existing to capture images of the environment, which can be later fused with the facility may simply not exist. These observations point to the need for 3D data to aid in the modeling process [6]. methods to create BIMs that represent a facility's as-built/as-is conditions. We use the term “as-built” to mean “as-built/as-is” for brevity hereafter. 1.1.2. Data preprocessing The creation of an as-built BIM involves measuring the geometry and The point clouds from each scan are initially represented in the appearance of an existing facility and transforming those measurements scanner's local coordinate frame. All of the data need to be aligned in a into a high-level, semantically rich representation. The goals of this common, global coordinate system through a process known as article are threefold: 1) to document the state-of-the-art