Reality Modeling Webinar

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Esther Thompson
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1 Reality Modeling Webinar Leveraging 3D Reality Meshes for Real-Time Asset Management and Monitoring

2 What is Reality Modeling? Images & video?

3 What is Reality Modeling? Images & video As-built drawings & models

4 What is Reality Modeling? Images & video As-built drawings & models Point clouds

5 What is Reality Modeling? Images & video As-built drawings & models Point clouds Image-based reconstruction

6 Image-based Reconstruction: ContextCapture

7 Image-based Reconstruction: Process CAPTURE PROCESS CONSUME Edit Design Vizualize and Animate Extract Features Reality Model Scalable Mesh Capture Specialist

8 Image-based Reconstruction: Scalable Mesh Multi-resolution textured mesh

9 Image-based Reconstruction: Scalable Mesh Multi-resolution textured mesh

10 Image-based Reconstruction: Scalable Mesh Multi-resolution textured mesh

11 Image-based Reconstruction: Scalable Mesh Multi-resolution textured mesh

12 Image-based Reconstruction: Scalable Mesh Multi-resolution textured mesh

13 Image-based Reconstruction: Scalable Mesh Multi-resolution textured mesh

14 Image-based Reconstruction: Scalable Mesh Multi-resolution, textured mesh Stream from Cloud Browser, Desktop app View, measure, animate

Image-based Reconstruction: Scalable Mesh Multi-resolution, textured mesh Stream from Cloud Browser, Desktop app View, measure, animate Open in Bentley s

15 Image-based Reconstruction: Scalable Mesh Multi-resolution, textured mesh Stream from Cloud Browser, Desktop app View, measure, animate Open in Bentley s MicroStation CONNECT Stream directly from the Cloud Geo-located High performance graphics Move, rotate, scale, etc. Insert new objects Non-destructive editing

16 Image-based Reconstruction: Scalable Mesh Multi-resolution, textured mesh Stream from Cloud Browser, Desktop app View, measure, animate Open directly in Bentley Systems MicroStation Stream directly from the Cloud, no conversion required Geo-located High performance graphics Move, rotate, scale, etc. Insert new objects Non-destructive editing Object classification

Image-based Reconstruction: Scalable Mesh Multi-resolution, textured mesh Stream from Cloud Browser, Desktop app View, measure, animate Open directly in Bentley Systems MicroStation

17 Image-based Reconstruction: Scalable Mesh Multi-resolution, textured mesh Stream from Cloud Browser, Desktop app View, measure, animate Open directly in Bentley Systems MicroStation Stream directly from the Cloud, no conversion required Geo-located High performance graphics Move, rotate, scale, etc. Insert new objects Non-destructive editing Object classification

18 Background Penn State s objective was to create a virtual campus A 3D interface to multiple sources of campus information A tool for multiple use cases Financial Design Models Construction Models Project Management Space Management Maintenance Management Images provided by Penn State GIS

19 Background Penn State s objective was to create a virtual campus A 3D interface to multiple sources of campus information A tool for multiple use cases Sponsored by: Dept. of Architectural Engineering (Dr. John Messner) Office of Physical Plant (Dr. Craig Dubler) Met with Real World Capture to explore the possibility of using reality models to support the virtual campus concept Modeling with standard BIM tools was not practical ContextCapture from Bentley Systems appeared to be a cost effective solution Original project scope: Create a base reality model of the main campus in State College, PA Integrate multiple sources of information with the reality model: Basic building information Work order information from Maximo maintenance management system and supporting documents Facility management information Underground utilities

Step 1: Image Capture Image capture was accomplished from a manned

flying at 1000+ feet above the ground Flight time was approximately 2

36 megapixel resolution The camera used a 70-200mm f/2.

20 Step 1: Image Capture Image capture was accomplished from a manned aircraft using a hand held DSLR camera: Aircraft was a Cessna 172 flying at feet above the ground Flight time was approximately 2 hours The camera was a Nikon D810 with a 35mm sensor Photos taken at 36 megapixel resolution The camera used a mm f/2.8 lens Photos were taken by the passenger out the aircraft window 2488 photos were taken

21 Step 2: Model Processing with ContextCapture The images were processed with ContextCapture Basic statistics Processing required approximately 48 hours on a 6 computer cluster The resulting reality model (.3mx) is approximately 3.1 gigabytes in size The model was initially posted to a web server where it could be accessed via a URL for review

Step 2: Model Processing with ContextCapture The images were processed with ContextCapture Basic statistics Processing required approximately 48 hours on a 6 computer

22 Step 2: Model Processing with ContextCapture The images were processed with ContextCapture Basic statistics Processing required approximately 48 hours on a 6 computer cluster The resulting reality model (.3mx) is approximately 3.1 gigabytes in size The model was initially posted to a web server where it could be accessed via a URL for review

23 Step 3: Opening the reality model with MicroStation Using the reality mesh capabilities in MicroStation CONNECT to attach the reality model to a design file.

Step 4: Classifying model objects with GIS data Penn State provided GIS files (ArcGIS shape files) with 2D footprints for: Buildings Roads Parking lots GIS data for the utilities

24 Step 4: Classifying model objects with GIS data Penn State provided GIS files (ArcGIS shape files) with 2D footprints for: Buildings Roads Parking lots GIS data for the utilities was downloaded from the State College, PA public web site MicroStation CONNECT reality mesh tools used this data to classify the reality model into separate, identifiable objects

25 Step 4: Classifying model objects with GIS data Building footprint classifiers (approximately 900 campus buildings)

28 Step 4: Classifying model objects with GIS data Building footprint classifiers (approximately 900 campus buildings)

29 Step 4: Classifying model objects with GIS data Utility GIS data painted on the surface of the reality model

30 Step 4: Classifying model objects with GIS data Utility GIS data painted on the surface of the reality model

31 Step 5: Integrating work orders and documents Penn State Office of Physical Plant provided the meta-data for 4300 Maximo work orders Work order number Building location Work description Type of work Current status etc. Created folders in ProjectWise containing supporting documents for each type of work Preventative maintenance Corrective maintenance Regular service Troubleshooting Integrated the information with the reality model with Bentley s i-model Transformer 900 buildings 4300 work orders 4 ProjectWise folders w/documents

34 Step 6: Photo Navigation The photos used to create the reality model were uploaded to ProjectWise and then accessed with the 3D photo navigation capabilities of MicroStation CONNECT.

37 Step 6: Photo Navigation The photos used to create the reality model were uploaded to ProjectWise and then accessed with the 3D photo navigation capabilities of MicroStation CONNECT.

38 Step 7: Integrate new construction information Penn State is planning a new campus building for the Chemical Engineering department. The architect contracted by Penn State to design the building provided the Revit BIM model created during their design process. Using the i-model Plugin for Revit, an i-model was created for the building. Using MicroStation CONNECT, this i-model was integrated into the campus reality model to visualize how the new building would look in the campus.

39 Step 7: Integrate new construction information Penn State is planning a new campus building for the Chemical Engineering department. The architect contracted by Penn State to design the building provided the Revit BIM model created during their design process. Using the i-model Plugin for Revit, an i-model was created for the building. Using MicroStation CONNECT, this i-model was integrated into the campus reality model to visualize how the new building would look in the campus.

40 Current Status Project considered a success Validated the use of a reality model as the basis for a virtual campus Considering options for deployment of basic capability Continuing work PhD student continuing research in application of reality model Monitoring new construction Augmenting reality model with ground photographs

41 Image-based Reconstruction Opportunities 3D makes a difference Reality capture by other means is expensive or not practical Change over time is important Visual detail and/or completeness is important Want to evaluate proposed changes Physical access is limited or expensive Need to integrate multiple types of media Desire access to multiple information sources from single interface

42 Reality Modeling Challenges Technology Issues Image-based reconstruction vs. point clouds Accuracy Interiors Logistical Issues Limitations on use of drones Availability of survey partners Perceptions Magic? Accuracy? Value?

43 Download a complimentary trial of ContextCapture

44 Real World Capture

46 Services Plan and conduct Photo Acquisition UAS, Manned Aircraft, Ground, Hybrid Model Creation Processing the photos into an accurate model Geolocate, Scale, Refine Integrate data, photos Tagging Asset Information Photo Indexing Lightweight Web Browser Interface Existing Conditions Model and Mesh for Downstream Conceptual Design Integrating GIS Data

47 Providing models and data integration for Better communication navigate and measure Construction measure value for in-place value of construction and progress Fast and Accurate Surveys Integration with GIS, Maintenance, and other enterprise systems Complete and Safe Inspection leverage the model as an index to the photos Detailed Asset Management know what you have, where and its condition

48 Questions?

49 Reality Modeling Webinar Leveraging 3D Reality Meshes for Real-Time Asset Management and Monitoring

ADVANCING REALITY MODELING WITH CONTEXTCAPTURE

ADVANCING REALITY MODELING WITH CONTEXTCAPTURE Knowing the existing conditions of a project is a key asset in any decision process. Governments need to better know their territories, through mapping operations,