Toward automated generation of parametric BIMs based on hybrid video and laser scanning data Ioannis Brilakis a, * , Manolis Lourakis b , Rafael Sacks c , Silvio Savarese d , Symeon Christodoulou e , Jochen Teizer a , Atefe Makhmalbaf a a Georgia Institute of Technology, USA b Foundation for Research and Technology, Greece c Technion Israel Institute of Technology, Israel d University of Michigan, USA e University of Cyprus, Cyprus article info Article history: Received 21 June 2010 Accepted 24 June 2010 Available online 16 July 2010 Keywords: Building information modeling Computer vision Machine learning Image processing Videogrammetry abstract Only very few constructed facilities today have a complete record of as-built information. Despite the growing use of Building Information Modelling and the improvement in as-built records, several more years will be required before guidelines that require as-built data modelling will be implemented for the majority of constructed facilities, and this will still not address the stock of existing buildings. A tech- nical solution for scanning buildings and compiling Building Information Models is needed. However, this is a multidisciplinary problem, requiring expertise in scanning, computer vision and videogrammetry, machine learning, and parametric object modelling. This paper outlines the technical approach proposed by a consortium of researchers that has gathered to tackle the ambitious goal of automating as-built modelling as far as possible. The top level framework of the proposed solution is presented, and each pro- cess, input and output is explained, along with the steps needed to validate them. Preliminary experi- ments on the earlier stages (i.e. processes) of the framework proposed are conducted and results are shown; the work toward implementation of the remainder is ongoing. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction The current state-of-the-art approach to collecting, organizing and integrating as-built data of a constructed facility into a single data structure is to model it using building information modelling (BIM) tools [1]. This approach generates parametric building mod- els by producing logical building objects and the parametric rela- tionships among them. The process starts by collecting spatial data on site through state-of-the-art surveying technologies, such as laser scanning (LIDAR) and photogrammetry. The resulting spa- tial data must then be manually stitched into a 3D surface with some algorithmic help for fine stitching. The points on the 3D sur- face are then manually replaced by objects, by having an operator observe the data, identify each object type, search for it in a data- base of standardized objects, and fit it on the surface with some help from fitting algorithms for optimal fitting. Following that, any as-built attributes can be assigned to each object manually. Although this process is significantly assisted by recent techno- logical advancements, most of it remains manual. Researchers along with professional modellers such as VECO [2] and Reality Measurements [3] have reported that more than two thirds of the efforts needed to model even simple facilities are spent on manual conversion of the surface data to a BIM. This problem re- sults in significant cost and effort that is needed to convert the sensed surface of constructed facilities to the desired model, which undermines any benefits of automated spatial modelling for the majority of facilities. According to studies by Minhindu and Arayici [4] and Young et al. [5], BIM adoption is growing in some countries such as U.S., Denmark, Finland and Norway. However, as McCarthy [6] had predicted, for small construction projects, the net savings can barely justify adoption and utilization of this technology. As a result, the penetration of innovative spatial modelling technolo- gies to smaller projects and companies in the Architecture, Engi- neering & Construction (AEC) industry is slow and they will wait unless significant savings can occur. This paper presents a novel framework that holds promise to automate almost entirely the generation of as-built parametric BIMs of constructed facilities, ranging from residential housing to industrial structures. This framework uses spatial and visual data collected in the field to generate images and the 3D surface repre- sented as a point cloud. The next step is to stitch images together in order to integrate them into a single 3D representation. Then, by analyzing geometric surface and surface texture information 1474-0346/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.aei.2010.06.006 * Corresponding author. Tel.: +1 4048949881. E-mail address: brilakis@gatech.edu (I. Brilakis). Advanced Engineering Informatics 24 (2010) 456–465 Contents lists available at ScienceDirect Advanced Engineering Informatics journal homepage: www.elsevier.com/locate/aei