FIATECH

Presented by Mike Zink, Senior Product Manager,  Open Tools, Siemens Industry Software and Erica Simmons, Global Marketing Manager, Energy and Utilities, Siemens Industry Software

Reserve your webinar seat now at https://www1.gotomeeting.com/register/134305041

The cost of poor interoperability has been valued at greater than the entire market capitalization of the CAD industry, annually. Typically data is stored in a format that can only be read by the software program used to create the original files, and is not accessible to consumers who may not have the native software applications. JT is an open platform for visualization, collaboration and data sharing that is designed to be inclusive and welcoming to all organizations involved in industry worldwide. As testimony to JT's openness, in 2009 the International Organization for Standardization (ISO) accepted the JT File Format Specification for publication as an ISO Publicly Available Specification (PAS) for 3D Visualization.

The JT data representation is:
• a rich data model with robust entity support
• a high-performance, compact persistence archive format for graphics data
• the best-in-class for supporting large assembly ⁄ model interactive capabilities

In many cases, JT maybe the only format common to the major engineering applications that might be used in an enterprise. Join this web seminar to learn more about JT and the community of corporate users that use JT to solve common problems and the growing list of software vendors that support them.

About JT and the JT Open Program

JT Open is a unique program to help members leverage the benefits of open collaboration across the extended enterprise through the adoption of the JT format, a technology that makes it possible to view and share product and project information throughout the lifecycle. JT Open partnerships with companies like Microsoft and Adobe will continue to increase this trend. Join us for the JT Open 2010 International conference in Orlando, FL on September 13-14, 2010. For more information visit, www.jtopen.com.

Presented by Reg Hunter, an independent Productivity Improvement Strategist and an Engineered Systems Solution Designer

Presented by Mani Golparvar-Fard, University of Illinois

**Recipient of the 2009 CETI Award in Outstanding Student Researcher

Download Presentation (Microsoft PowerPoint 85.9MB)

Download Supporting Video File (AVI 356MB)

Mr. Goldparvar-Fard's research focuses on exploring the application of unordered daily construction photo collections as an as-built data collection technique. Using these images, he explores how as-built scenes can be automatically reconstructed in 4 dimensions (3D + time) and integrated with 4D building information models. Integrating as-built and as-planned models generates D4AR - 4 dimensional augmented reality - models andin turn allows progress, safety, quality, and other site performance metrics to be tracked and analyzed. Furthermore, D4AR models jointly visualizes all sorts of performance deviations, which in turn facilitates communication and reporting of project performance.

Application of D4AR models allows remote and easy virtual walk through on the as-built scene, facilitates remote construction control decision making, minimizes the time required to discuss the as-built scene through quick and intuitive access to actual construction information and significantly cuts in travel time and cost for project executives, architects and owners. Using D4AR models, AEC professionals can remotely access a project, visualize integrated as-built and as-planned scene and assess progress, productivity, safety, and quality plus site logistics. The automated progress monitoring scheme built upon the D4AR models is the first probabilistic model for progress tracking and visualization of deviations that incorporates both as-planned models and unordered daily photographs in a principled way. Unlike other methods that focus on application of laser scanners or time-lapse photography, this approach is able to use existing information without adding the burden of explicit data collection on project management. It also reports competitive monitoring accuracies compared to laser scanner-based techniques, especially at presence of occlusions. From a research perspective, the D4AR models allow implementing computer vision and image processing techniques to automatically track onsite productivity, safety, quality and site logistics; new daily site photographs can be registered on-the-fly, keeping the model flexible and generating new changes quickly; and generate new augmented reality occlusion removal techniques by reasoning about occupation in the scene. In addition the probabilistic automated progress monitoring model is easily extendable to incorporate visual appearance information and can be used for detection of progress at interiors; especially at places where access of laser scanners is limited; and finally as-built semantics can be extracted from the content of D4AR imagery. This step can lead to automated reconstruction of 3D BIM models for those existing facilities which their as-built models are not readily available (e.g., to be used for life cycle energy analysis).