CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
supported by the sibling associations ACADIA, CAADRIA, eCAADe, SIGraDi, ASCAAD and CAAD futures

id caadria2015_188
authors Krakhofer, Stefan and Martin Kaftan
year 2015
title Augmented Reality Design Decision Support Engine for the Early Building Design Stage
source Emerging Experience in Past, Present and Future of Digital Architecture, Proceedings of the 20th International Conference of the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA 2015) / Daegu 20-22 May 2015, pp. 231-240
summary Augmented reality has come a long way and experienced a paradigm shift in 1999 when the ARToolKit was released as open source. The nature of interaction between the physical world and the virtual-world has changed forever. Fortunately for the AECO industry, the transition from traditional Computer Aided Design to virtual building design phrased as Building Information Modeling has created a tremendous potential to adopt Augmented Reality. The presented research is situated in the early design stage of project inception and focuses on supporting informed collective decision-making, characterized by a dynamic back and forth analytical process generating large amounts of data. Facilitation aspects, such as data-collection, storage and access to enable comparability and evaluation are crucial for collective decision-making. The current research has addressed these aspects by means of data accessibility, visualization and presentation. At the core of the project is a custom developed Augmented Reality framework that enables data interaction within the design model. In order to serve as a collaborative decision support engine, the framework also allows multiple models and their datasets to be displayed and exercised simultaneously. The paper demonstrates in the case study the successful application of the AR tool during collaborative design decision meetings.
keywords Augmented Reality; Design Decision Support; Data Visualization.
series CAADRIA
email skrakhof@cityu.edu.hk
full text file.pdf (1,487,509 bytes)
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