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Availability of Mobile Augmented Reality System
         for Urban Landscape Simulation




Tomohiro Fukuda, Tian Zhang, and Nobuyoshi Yabuki
Division of Sustainable Energy and Environmental Engineering,
   Graduate School of Engineering, Osaka University, Japan
Contents
1. Introduction
2. Developed Mobile AR
3. Comparative verification of landscape
   simulation methods
  1.   Experimental Outline
  2.   Differences between Cloud-VR and mobile AR in Evaluation
  3.   Results and Discussion

4. Conclusion




                                                                  2
Contents
1. Introduction
2. Developed Mobile AR
3. Comparative verification of landscape
   simulation methods
  1.   Experimental Outline
  2.   Differences between Cloud-VR and mobile AR in Evaluation
  3.   Results and Discussion

4. Conclusion




                                                                  3
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
1. Introduction
1.1 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
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
1. Introduction
1.2 Previous Study
2. 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
1. Introduction
1.3 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
Contents
1. Introduction
2. Developed Mobile AR
3. Comparative verification of landscape
   simulation methods
  1.   Experimental Outline
  2.   Differences between Cloud-VR and mobile AR in Evaluation
  3.   Results and Discussion

4. Conclusion




                                                                  9
2. Developed Mobile AR
2.1 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
2. Developed Mobile AR
2.2 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 of
Google Maps             gyroscope          video camera

Input of DEM
                    Angle information      Capture of live
                       acquisition          video image        Distortion        Calibration
  Position
information
 acquisition

 Definition of position and angle
information 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
2. Developed Mobile AR
2.2 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 of
Google Maps             gyroscope          video camera

Input 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 plane
information 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
2. Developed Mobile AR
2.2 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 of
Google Maps             gyroscope          video camera

Input of DEM
                    Angle information      Capture of live
                       acquisition          video image
  Position
information
 acquisition

 Definition of position and angle
information 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
2. Developed Mobile AR
2.2 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 of
Google Maps             gyroscope          video camera

Input of DEM
                    Angle information      Capture of live
                       acquisition          video image       roll
  Position                                                                           pitch
information
 acquisition

 Definition of position and angle
information 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
2. Developed Mobile AR
2.2 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 of
Google Maps             gyroscope          video camera

Input of DEM
                    Angle information      Capture of live
                       acquisition          video image
  Position
information
 acquisition
                                                             2. The position data (longitude,
 Definition of position and angle                               latitude) on the current location
information 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
2. Developed Mobile AR
2.2 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 of
Google Maps             gyroscope          video camera

Input of DEM
                    Angle information      Capture of live
                       acquisition          video image
  Position
information
 acquisition

 Definition of position and angle
information on CG virtual camera

  Superposition to live video image and 3DCG model

                   Display of AR image

                    Save of AR image




                                                                                  16
Contents
1. Introduction
2. Developed Mobile AR
3. Comparative verification of landscape
   simulation methods
  1.   Experimental Outline
  2.   Differences between Cloud-VR and mobile AR in Evaluation
  3.   Results and Discussion

4. Conclusion




                                                                  17
3. Comparative verification of landscape simulation methods

3.1 Experimental Outline -1
The landscape simulation method of a mobile AR was verified through
comparative experiments using photo montage and VR, which are existing
methods. In order to use the same conditions as mobile AR, a cloud
computing type VR (cloud-VR) which can run Android OS was applied.

 Experimental Methodology
1. 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.
3. Comparative verification of landscape simulation methods

3.1 Experimental Outline -2
 Experimental photos and outputs




   Photo montage            Cloud-VR                        Mobile AR        19
20
3. Comparative verification of landscape simulation methods

3.1 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
3. Comparative verification of landscape simulation methods

3.1 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
3. Comparative verification of landscape simulation methods

3.1 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
3. Comparative verification of landscape simulation methods

3.2 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
3. Comparative verification of landscape simulation methods

3.3 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
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 user's 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.
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.
Contents
1. Introduction
2. Developed Mobile AR
3. Comparative verification of landscape
   simulation methods
  1.   Experimental Outline
  2.   Differences between Cloud-VR and mobile AR in Evaluation
  3.   Results and Discussion

4. Conclusion




                                                                  28
4. Conclusion

4.1 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 user's 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
4. Conclusion

4.2 Future Work
 A future work should attempt to improve the optical integrity of
  the AR system.




                                                                      30
Thank you for your attention!


   E-mail:    fukuda@see.eng.osaka-u.ac.jp
   Twitter:   fukudatweet
Facebook:     Tomohiro Fukuda
 Linkedin:    Tomohiro Fukuda

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Availability of Mobile Augmented Reality System for Urban Landscape Simulation

  • 1. Availability of Mobile Augmented Reality System for Urban Landscape Simulation Tomohiro Fukuda, Tian Zhang, and Nobuyoshi Yabuki Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Japan
  • 2. Contents 1. Introduction 2. Developed Mobile AR 3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion 4. Conclusion 2
  • 3. Contents 1. Introduction 2. Developed Mobile AR 3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion 4. Conclusion 3
  • 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. 1. Introduction 1.1 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. 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. 1. Introduction 1.2 Previous Study 2. 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. 1. Introduction 1.3 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. Contents 1. Introduction 2. Developed Mobile AR 3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion 4. Conclusion 9
  • 10. 2. Developed Mobile AR 2.1 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. 2. Developed Mobile AR 2.2 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 of Google Maps gyroscope video camera Input of DEM Angle information Capture of live acquisition video image Distortion Calibration Position information acquisition Definition of position and angle information 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. 2. Developed Mobile AR 2.2 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 of Google Maps gyroscope video camera Input 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 plane information 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. 2. Developed Mobile AR 2.2 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 of Google Maps gyroscope video camera Input of DEM Angle information Capture of live acquisition video image Position information acquisition Definition of position and angle information 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. 2. Developed Mobile AR 2.2 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 of Google Maps gyroscope video camera Input of DEM Angle information Capture of live acquisition video image roll Position pitch information acquisition Definition of position and angle information 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. 2. Developed Mobile AR 2.2 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 of Google Maps gyroscope video camera Input of DEM Angle information Capture of live acquisition video image Position information acquisition 2. The position data (longitude, Definition of position and angle latitude) on the current location information 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. 2. Developed Mobile AR 2.2 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 of Google Maps gyroscope video camera Input of DEM Angle information Capture of live acquisition video image Position information acquisition Definition of position and angle information on CG virtual camera Superposition to live video image and 3DCG model Display of AR image Save of AR image 16
  • 17. Contents 1. Introduction 2. Developed Mobile AR 3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion 4. Conclusion 17
  • 18. 3. Comparative verification of landscape simulation methods 3.1 Experimental Outline -1 The landscape simulation method of a mobile AR was verified through comparative experiments using photo montage and VR, which are existing methods. In order to use the same conditions as mobile AR, a cloud computing type VR (cloud-VR) which can run Android OS was applied.  Experimental Methodology 1. 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. 3. Comparative verification of landscape simulation methods 3.1 Experimental Outline -2  Experimental photos and outputs Photo montage Cloud-VR Mobile AR 19
  • 20. 20
  • 21. 3. Comparative verification of landscape simulation methods 3.1 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. 3. Comparative verification of landscape simulation methods 3.1 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. 3. Comparative verification of landscape simulation methods 3.1 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. 3. Comparative verification of landscape simulation methods 3.2 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. 3. Comparative verification of landscape simulation methods 3.3 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. 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 user's 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. 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. Contents 1. Introduction 2. Developed Mobile AR 3. Comparative verification of landscape simulation methods 1. Experimental Outline 2. Differences between Cloud-VR and mobile AR in Evaluation 3. Results and Discussion 4. Conclusion 28
  • 29. 4. Conclusion 4.1 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 user's 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. 4. Conclusion 4.2 Future Work  A future work should attempt to improve the optical integrity of the AR system. 30
  • 31. Thank you for your attention! E-mail: fukuda@see.eng.osaka-u.ac.jp Twitter: fukudatweet Facebook: Tomohiro Fukuda Linkedin: Tomohiro Fukuda