Automatic reconstruction of as-built building information models from laser-scanned point clouds: A review of related techniques

Building information models (BIMs) are maturing as a new paradigm for storing and exchanging knowledge 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 and the resulting point cloud can be manually processed to create an as-built BIM — a time-consuming, subjective, and error-prone process that could benefit significantly from automation. This article surveys techniques developed in civil engineering and computer science that can be utilized to automate the process of creating as-built BIMs. We sub-divide the overall process into three core operations: geometric modeling, object recognition, and object relationship modeling. We survey the state-of-the-art methods for each operation and discuss their potential application to automated as-built BIM creation. We also outline the 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.

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Automatic Reconstruction of As-Built Building Information Models from Laser-Scanned Point Clouds: A Review of Related Techniques | NIST

http://www.ri.cmu.edu/pub_files/2011/6/2011-antonio-isarc-3d-modeling-v6.pdf

Automatic Creation of Semantically Rich 3D Building Models from Laser Scanner Data

Laser scanners are increasingly used to create semantically rich 3D models of buildings for civil engineering applications such as planning renovations, space usage planning, and building maintenance. Currently these models are created manually – a time-consuming and error-prone process. This paper presents a method to automatically convert the raw 3D point data from a laser scanner positioned at multiple locations throughout a building into a compact, semantically rich model. Our algorithm is capable of identifying and modeling the main structural components of an indoor environment (walls, floors, ceilings, windows, and doorways) despite the presence of significant clutter and occlusion, which occur frequently in natural indoor environments. Our method begins by extracting planar patches from a voxelized version of the input point cloud. We use a conditional random field model to learn contextual relationships between patches and use this knowledge to automatically label patches as walls, ceilings, or floors. Then, we perform a detailed analysis of the recognized surfaces to locate windows and doorways. This process uses visibility reasoning to fuse measurements from different scan locations and to identify occluded regions and holes in the surface. Next, we use a learning algorithm to intelligently estimate the shape of window and doorway openings even when partially occluded. Finally, occluded regions on the surfaces are filled in using a 3D inpainting algorithm. We evaluated the method on a large, highly cluttered data set of a building with forty separate rooms yielding promising results.

Semantic 3D models of buildings encode the geometry as well as the identity of key components of a facility, such as walls, floors, and ceilings. Manually constructing such a model is a time-consuming and error-prone process. Our goal is to automate this process using 3D point data from a laser scanner. Our hypothesis is that contextual information is important to reliable performance in unmodified environments, which are often highly cluttered. We use a Conditional Random Field (CRF) model to discover and exploit contextual information, classifying planar patches extracted from the point cloud data. We compare the results of our context-based CRF algorithm with a context-free method based on L2 norm regularized Logistic Regression (RLR). We find that using certain contextual information along with local features leads to better classification results.

/pdf/using-context-to-create-semantic-3d-models-of-indoor-1k4hw2fp56.pdf

Using Context to Create Semantic 3D Models of Indoor Environments.

This doctoral thesis presents new techniques for improving the structural quality of automatically-acquired architectural 3D models. Common architectural properties such as parallelism and orthogonality of walls and linear structures are exploited. The locations of features such as planes and 3D lines are extracted from the model by using a probabilistic technique (RANSAC). The relationships between the planes and lines are inferred automatically using a knowledge-based architectural model. A numerical algorithm is then used to optimise the position and orientations of the features taking constraints into account. Small irregularities in the model are removed by projecting the irregularities onto the features. Planes and lines in the resulting model are therefore aligned properly to each other, and so the appearance of the resulting model is improved. Our approach is demonstrated using noisy data from both synthetic and real scenes.

Helmut Cantzler

Papers

Improving architectural 3D reconstruction by constrained modelling