Availability of Mobile Augmented Reality System for Urban Landscape Simulation

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Abstract. This research presents the availability of a landscape simulation method for a mobile AR (Augmented Reality), comparing it with photo montage and VR (Virtual Reality) which are the main existing methods. After a pilot experiment with 28 subjects in Kobe city, a questionnaire about three landscape simulation methods was implemented. In the results of the questionnaire, the mobile AR method was well evaluated for reproducibility of a landscape, operability, and cost. An evaluation rated as better than equivalent was obtained in comparison with the existing methods. The suitability of mobile augmented reality for landscape simulation was found to be high.

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Availability of Mobile Augmented Reality System for Urban Landscape Simulation

  1. 1. Availability of Mobile Augmented Reality System for Urban Landscape SimulationTomohiro Fukuda, Tian Zhang, and Nobuyoshi YabukiDivision of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Japan
  2. 2. Contents1. Introduction2. Developed Mobile AR3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion4. Conclusion 2
  3. 3. Contents1. Introduction2. Developed Mobile AR3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion4. Conclusion 3
  4. 4. 1. Introduction 1.1 Motivation -1  In recent years, the need for landscape simulation has been growing. A review meeting of future landscape is carried out on a planned construction site in addition to being carried out in a room.  It is difficult for stakeholders to imagine concretely such an image that is three-dimensional and does not exist. A landscape visualization method using Computer Graphics (CG) and Virtual Reality (VR) has been developed.  However, this method requires much time and expense to make a 3D model such as the present terrain and artificial material in addition to the subject of the landscape assessment. Moreover, since consistency with real space is not achieved when using VR on a planned construction site, it has the problem that a reviewer cannot get an immersive experience.A landscape study on site VR capture 4
  5. 5. 1. Introduction1.1 Motivation -2 In this research, the authors focus Augmented Reality (AR) which can superimpose an actual landscape acquired with a video camera and 3DCG. When AR is used, a landscape assessment object will be included in the present surroundings. Thereby, a drastic reduction of the time and expense involved in carrying out 3DCG modeling of the present surroundings can be expected. A smartphone is widely available on the market level. Sekai Camera Web Smartphone Market in Japan http://sekaicamera.com/ 5
  6. 6. 1. Introduction モバイル型景観ARの進化1.2 Previous Study  In AR, realization of geometric consistency with a video image of an actual landscape and CG is an important feature 1. Use of physical sensors such as GPS (Global Positioning System) and gyroscope. To realize highly precise geometric consistency, special hardware which is expensive is required. Image Sketch (2005) 2006 ©2012 Tomohiro Fukuda, Osaka-U 6
  7. 7. 1. Introduction1.2 Previous Study2. Use of an artificial marker. Since an artificial marker needs to be always visible by the AR camera, the movable span of a user is limited. Moreover, to realize high precision, it is necessary to use a large artificial marker. Yabuki, N., et al.: 2011, An invisible height evaluation system for building height regulation to preserve good landscapes using augmented reality, Automation in Construction, Volume 20, Issue 3, 228-235. artificial marker 7
  8. 8. 1. Introduction1.3 Aim The authors have developed and verified SOAR (Sensor Oriented Mobile AR) system which realizes geometric consistency using GPS, a gyroscope and a video camera which are mounted in a smartphone [1]. The authors have also developed and verified GOAR (GIS Oriented Mobile AR) system which uses GIS to obtain position data instead of GPS [2]. A low cost AR system with high flexibility is realizable. In this research, the availability of landscape simulation method of a mobile AR is considered, comparing with a photo montage and VR which are existing methods.1. Fukuda, et al.: SOAR: Sensor oriented Mobile Augmented Reality for Urban Landscape Assessment, Proceedings of the 17th International Conference on Computer Aided Architectural Design Research in Asia (CAADRIA2012), pp. 387-396 (2012)2. Fukuda, et al.: GOAR: GIS oriented Mobile Augmented Reality for Urban Landscape Assessment, 4th International Conference on Communications, Mobility, and Computing (CMC 2012), pp. 183-186 (2012) 8
  9. 9. Contents1. Introduction2. Developed Mobile AR3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion4. Conclusion 9
  10. 10. 2. Developed Mobile AR2.1 Developed Mobile AR System Standard Spec Smartphone: GALAPAGOS 003SH (Softbank Mobile Corp.) Development Language: OpenGL-ES(Ver.2.0),Java(Ver.1.6) Development Environment: Eclipse Galileo(Ver.3.5) Location Estimation Technology: GIS includes Google Maps API and Digital Elevation Model (DEM) which is 10 m mesh size (GOAR) Video Camera Spec of 003SH OS Android™ 2.2 Qualcomm®MSM8255 CPU Snapdragon® 1GHz ROM:1GB Memory RAM:512MB Weight ≒140g Size ≒W62×H121×D12mm Display Size 3.8 inch Resolution 480×800 pixel 003SH 10
  11. 11. 2. Developed Mobile AR2.2 System Flow -1 While the CG model realizes Calibration of a video camera ideal rendering by the Definition of landscape assessment 3DCG model perspective drawing method, rendering of a video camera Activation of AR system produces distortion. Selection of 3DCG model Starting of Activation of Activation ofGoogle Maps gyroscope video cameraInput of DEM Angle information Capture of live acquisition video image Distortion Calibration Positioninformation acquisition Definition of position and angleinformation on CG virtual camera Superposition to live video image and 3DCG model Display of AR image Save of AR image Calibration of the video camera 11 using Android NDK-OpenCV
  12. 12. 2. Developed Mobile AR2.2 System Flow -2 3DCG Model Calibration of a video camera Definition of landscape assessment 3DCG model Geometry, Texture, Unit Activation of AR system Selection of 3DCG model 3DCG model allocation file Starting of Activation of Activation ofGoogle Maps gyroscope video cameraInput of DEM Angle information Capture of live acquisition video image 3DCG model name, File name, Position Position data (longitude, latitude,information acquisition orthometric height), Degree of rotation angle, and Zone Definition of position and angle number of the rectangular planeinformation on CG virtual camera Superposition to live video image and 3DCG model 3DCG model arrangement information file Display of AR image Save of AR image Number of the 3DCG model allocation information file, 12 Each name
  13. 13. 2. Developed Mobile AR2.2 System Flow -3 Calibration of a video camera Definition of landscape assessment 3DCG model Activation of AR system Selection of 3DCG model Starting of Activation of Activation ofGoogle Maps gyroscope video cameraInput of DEM Angle information Capture of live acquisition video image Positioninformation acquisition Definition of position and angleinformation on CG virtual camera Superposition to live video image and 3DCG model Display of AR image Save of AR image GUI of the Developed System 13
  14. 14. 2. Developed Mobile AR2.2 System Flow -4 Calibration of a video camera Definition of landscape assessment 3DCG model yaw Activation of AR system Selection of 3DCG model Starting of Activation of Activation ofGoogle Maps gyroscope video cameraInput of DEM Angle information Capture of live acquisition video image roll Position pitchinformation acquisition Definition of position and angleinformation on CG virtual camera Coordinate System of Developed AR system Superposition to live video image and 3DCG model Display of AR image Save of AR image 14
  15. 15. 2. Developed Mobile AR2.2 System Flow -5 Calibration of a video camera Definition of landscape assessment 3DCG model Activation of AR system Selection of 3DCG model 1. The user tap the current location on Google Maps Starting of Activation of Activation ofGoogle Maps gyroscope video cameraInput of DEM Angle information Capture of live acquisition video image Positioninformation acquisition 2. The position data (longitude, Definition of position and angle latitude) on the current locationinformation on CG virtual camera is obtained Superposition to live video image and 3DCG model Display of AR image Save of AR image 3. Altitude is created using position data (longitude, latitude) and DEM 15
  16. 16. 2. Developed Mobile AR2.2 System Flow -6 Calibration of a video camera Definition of landscape assessment 3DCG model Activation of AR system Selection of 3DCG model Starting of Activation of Activation ofGoogle Maps gyroscope video cameraInput of DEM Angle information Capture of live acquisition video image Positioninformation acquisition Definition of position and angleinformation on CG virtual camera Superposition to live video image and 3DCG model Display of AR image Save of AR image 16
  17. 17. Contents1. Introduction2. Developed Mobile AR3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion4. Conclusion 17
  18. 18. 3. Comparative verification of landscape simulation methods3.1 Experimental Outline -1The landscape simulation method of a mobile AR was verified throughcomparative experiments using photo montage and VR, which are existingmethods. In order to use the same conditions as mobile AR, a cloudcomputing type VR (cloud-VR) which can run Android OS was applied. Experimental Methodology1. A 3D model of a virtual project was created. In this research, a high-rise building (width: 40m, depth: 40m, height: 150m) and a wind power generator (height: 104m) were selected at varying distances (100m and 1200m) from a viewpoint. Moreover, the Tokyo Sky Tree (height: 634m) was selected at a distance of 1500m from the viewpoint.2. The operation of photo montage, Cloud-VR, and mobile AR was explained to the subjects.3. The subjects carried out the landscape study using photo montage for about two minutes, using Cloud-VR for about five minutes, and using a mobile AR for five minutes, in that order.4. After the experiment, a questionnaire about the three landscape simulation methods was implemented. The themes of the questionnaire were the reproducibility of the landscape, the operability of the system, and cost.
  19. 19. 3. Comparative verification of landscape simulation methods3.1 Experimental Outline -2 Experimental photos and outputs Photo montage Cloud-VR Mobile AR 19
  20. 20. 20
  21. 21. 3. Comparative verification of landscape simulation methods3.1 Experimental Outline -4 The viewpoint was the West Park (longitude: 34.672501111, latitude: 135.20194833, altitude: 4m) in Port Island, Kobe city.Regulation of building heights viewpoint Present state 21
  22. 22. 3. Comparative verification of landscape simulation methods3.1 Experimental Outline -5 There were 28 subjects, of which 75% were male (N=21) and 25% were female (N=7). Regarding age, 50% were in their 20s (N=14), 14% were in their 30s (N=4), 22% were in their 40s (N=6), and 14% were in their 50s (N=4). 54% subjects (N=15) had experience of using photo montage and/or VR for landscape study before and 46% subjects (N=13) had no such experience. 4, 14% 0, 0% S 20代 S 30代 6, 22% 14, 50% S 40代 S 50代 S 60代 4, 14% 22
  23. 23. 3. Comparative verification of landscape simulation methods3.1 Experimental Outline -6 The question items on the reproducibility of a landscape were "reality", "reproducibility", "scale grasp", "immersion", and "intuitiveness". The question items on operability were "easiness", "feedback", and "interactivity". The question items on cost were "expense", "creation time". The questionnaire result was scored using a 5-point scale. Five points was the best value. An independent t-test was performed according to simulation methods. Question items Large classification Small classification Reality Reproducibility Reproducibility Scale grasp Immersion Intuitiveness Easiness Operability Feedback Interactivity Expense Cost 23 Creation time
  24. 24. 3. Comparative verification of landscape simulation methods3.2 Differences between Cloud-VR and mobile AR in Evaluation  In regard to operability, mobile AR acquires the position data of CG virtual camera by GPS or GIS, and acquires the angle data of one with a gyroscope in real-time. Cloud-VR defines beforehand the position data and the angle data of view-points. Features such as fly-through, walk-through, parallel translation, rotation, etc. are operated via a GUI (Graphical User Interface) on a screen.  The screen size of the Cloud-VR is 10.1 inches, and the screen size of the mobile AR differs from 3.8 inches. However, the subjects considered the screens to be the same size.  At the time of the experiment, although texture mapping was used in the Cloud-VR, it was not used in the mobile AR. Since it is technically possible, the mobile AR was evaluated as if the texture mapping had been used. 24
  25. 25. 3. Comparative verification of landscape simulation methods3.3 Results and Discussion  As for the mobile AR, all the user groups gave a score of four or more points for "scale grasp", "immersion", "intuitiveness", "easiness", "feedback" and "interactivity". The score of 3.2 or more points was given for "reality", "reproducibility", "expense" and "creation time" which were the remaining items.  The items, “immersion", "feedback", and "interactivity” of photo montage and the items "expense", "creation time" of Cloud-VR were lower than three points. That is, mobile AR was given a high evaluation for all items. 25
  26. 26. 3. Comparative verification of landscape simulation methods 3.3 Results and Discussion: AR vs. Photo Montage  In all the user groups, a significant difference was obtained for the items "immersion", "easiness", "feedback", and "interactivity". In the experienced subjects, a further significant difference was obtained for "intuitiveness".  Why "feedback" and "interactivity" were given a high evaluation is considered. Both photo montage and mobile AR create a 3DCG model superimposed on a photo or live video. A photo montage is a two-dimensional picture and cannot respond to changes in the viewpoint position or direction during study. On the other hand, mobile AR can change the position and direction of the viewpoint corresponding to the users intention. Compa Rea Reprod Scale Immer Intuitiv Easin Feedb Intera Expen Creati on rison lity ucibility grasp sion eness ess ack ctivity se time Whole AR PM △△△ △△ △△△ △△△(N=28) AR VR ▼ △ △△ △ △△△ △△△ Experie AR PM △△△ △ △△ △△△ △△△ nced(N=15) AR VR ▼ △△ △△ △△Inexperi AR PM △ △ △△△ △△△ enced(N=13) AR VR △ △△△ △△△ △/▼: significant difference 5%, △△/▼▼: significant difference 1%, △△△/▼▼▼: significant 26 difference 0.1%, △: Left conditions have a large value., ▼: Right conditions have a large value.
  27. 27. 3. Comparative verification of landscape simulation methods 3.3 Results and Discussion: AR vs. VR  In all the user groups, a significant difference was obtained for the items "expense" and "creation time". VR needs to create all 3DCG models. AR creates only the subject in the 3DCG model. Therefore, when an object for landscape assessment created using a 3D model is not large, the cost performance of AR is high.  About "reproducibility", the reason the significant difference was obtained for the Cloud-VR may be associated with a problem of the optical integrity of AR. Since VR is created using a full 3DCG model, optical integrity is realized within the VR virtual space. On the other hand, AR differs in the influence of light on the 3DCG model and live video, and also differs in shade expression. Compa Rea Reprod Scale Immer Intuitiv Easin Feedb Intera Expen Creati on rison lity ucibility grasp sion eness ess ack ctivity se time Whole AR PM △△△ △△ △△△ △△△(N=28) AR VR ▼ △ △△ △ △△△ △△△ Experie AR PM △△△ △ △△ △△△ △△△ nced(N=15) AR VR ▼ △△ △△ △△Inexperi AR PM △ △ △△△ △△△ enced(N=13) AR VR △ △△△ △△△ △/▼: significant difference 5%, △△/▼▼: significant difference 1%, △△△/▼▼▼: significant 27 difference 0.1%, △: Left conditions have a large value., ▼: Right conditions have a large value.
  28. 28. Contents1. Introduction2. Developed Mobile AR3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion4. Conclusion 28
  29. 29. 4. Conclusion4.1 Conclusion For mobile AR, which is used as a smartphone platform, a score of 3.2 or more points was obtained for reproducibility of a landscape, operability, and cost. When comparing it with existing methods, mobile AR is evaluated as being better than equivalent. When mobile AR was compared with photo montage, a significant difference was obtained for "immersion" and "intuitiveness" of landscape reproducibility, and for "easiness", "feedback" and "interactivity" of operability. This was because mobile AR can respond to changes in the users viewpoint position or orientation, whereas photo montage cannot. When mobile AR was compared with Cloud-VR, a significant difference was obtained for "expense" and "creation time" of cost. VR needs to create all 3DCG models. AR creates only the subject using a 3DCG model. Therefore, when an object for landscape assessment created using a 3D model is not large, the cost performance of AR is high. 29
  30. 30. 4. Conclusion4.2 Future Work A future work should attempt to improve the optical integrity of the AR system. 30
  31. 31. Thank you for your attention! E-mail: fukuda@see.eng.osaka-u.ac.jp Twitter: fukudatweetFacebook: Tomohiro Fukuda Linkedin: Tomohiro Fukuda

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