Infrastructure As-Built Modeling
Videogrammetric Roof Surveying System for Digital Fabrication of Sheet Metal Roof Panels
I-CORPS Grant #1217201, PI: Ioannis Brilakis, $50,000
A structured hypothesis/validation approach was investigated in this project to develop a disposition plan for a videogrammetric surveying technology. The primary focus of this effort was on the possible use of this technology for as-built 3D documentation of construction sites; however, other potential markets such as on-site measurement of buildings, 3D visualization, augmented reality, and etc. were also investigated. Leading researcher: Man-Woo Park
CAREER: VISUAL PATTERN RECOGNITION MODELS FOR REMOTE SENSING OF CIVIL INFRASTRUCTURE
NSF Grant #0948415, PI: Ioannis Brilakis, $402,729
This is a project focused on fundamental research the will enable automated, model based recognition of construction objects. It entails the creation of visual pattern recognition models for a variety of construction object types. The purpose is to assist as-built modelers of facilities by automatically recognizing the common and more frequent objects automatically, leaving only the specialty items to the hands of the modeler. The validation of this project will be based on the software platform of Bentley Inc. , who is the industrial collaborator to this project. Results from this work can be found here. Leading researcher: Habib Fathi.
FP7-PEOPLE-2009-IRSES: BIMAUTOGEN
European Commision Grant, PI: Symeon Christodoulou, €300,510
The purpose of this project is to facilitate international collaboration and transfer of knowledge between the AutoBIM consortium members (I.Brilakis, GATech; R.Sacks, Technion; S.Christodoulou, UofCyprus; M.Lourakis, FORTH; S.Savarese, UofM; J.Teizer, GATech) and to implement and test whether a novel framework can be successfully used to generate parametric building models of buildings, ranging from residential housing to industrial facilities, almost entirely automatically. The project’s most significant contributions will be, not only the automation of several mundane and repetitive processes with the addition of visual and spatial pattern recognition concepts in the modelling workflow, but also the exchange of knowledge and the building of transatlantic research collaborations on cutting-edge and high-impact scientific projects through the exchange of interdisciplinary staff among partner institutions and joint training of research teams on thematic areas of common research interest. GRA positions available.
RECIPROCAL RECONSTRUCTION AND RECOGNITION FOR MODELING OF CONSTRUCTED FACILITIES
NSF Grant #1031329, PI: Ioannis Brilakis, $306,043
The research objective of this project is to evaluate whether a novel framework proposed by the PIs can progressively reconstruct a reinforced concrete frame structure into an object-oriented geometric model, for the purpose of automating the Building Information Model (BIM) making process of constructed facilities in a cost-effective manner. According to the proposed framework, the modeler videotapes the structure from all accessible angles to minimize occlusions. During this stage, the structural members (concrete columns and beams in this study) in the resulting stream of images are detected and their occupying region is marked in all images. These regions are used to establish correspondence at the object level across images, and solve the rough registration problem efficiently. Line-based structure from motion is then applied to the result to produce a rendered 3D view of the structure with the recognized regions marked. This loops back to the detection of structural members, which can now be also performed on the spatial data covered by the visually marked regions. The result is more robust element detection (by combining visual and spatial detection results), and consequently improved element matching and reconstruction. The resulting object-oriented model is expected to be an accurate 3D representation of the structure with the load bearing linear members detected. This model is provided to the modeler, who can then use it to complete the model making process. Leading researchers: Abbas Rashidi and Guangcong Zhang .