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CAADRIA2011, Newcastle, Australia




A STUDY OF VARIATION OF NORMAL
 OF POLYGONS CREATED BY POINT
 CLOUD DATA FOR ARCHITECTURAL
       RENOVATION FIELD


    TOMOHIRO FUKUDA, KENSUKE KITAGAWA
          and NOBUYOSHI YABUKI

Division of Sustainable Energy and Environmental Engineering,
                Graduate School of Engineering,
                    Osaka University, Japan
Contents
1. Introduction
2. 3D data modelling flow by 3DLS and this study
   1. 3D data modelling flow by using Poly-Opt
   2. Use of Poly-Opt in this study
3. Experimentation
   1. Experimental methodology
   2. Result and Discussion
4. Comparison measurement by using VR application
5. Conclusion




                                        ©2011 Tomohiro Fukuda, Osaka-U
Contents
1. Introduction
2. 3D data modelling flow by 3DLS and this study
   1. 3D data modelling flow by using Poly-Opt
   2. Use of Poly-Opt in this study
3. Experimentation
   1. Experimental methodology
   2. Result and Discussion
4. Comparison measurement by using VR application
5. Conclusion




                                        ©2011 Tomohiro Fukuda, Osaka-U
1. Introduction -1
   In the architectural renovation field and the urban design
    field, acquiring current 3D spatial data of cities, buildings,
    and rooms rapidly and in detail has become indispensable.
    The digitalization of SCMOD is also indispensable.
   Recently, researches using 3DLS (3D laser scanner) to acquire
    3D space have increased in the architecture, engineering,
    and construction fields.
   3DLS can provide accurate and complete details, but is
    expensive and not portable. Additionally, it is not efficient
    for scanning large objects.
   On the other hand, image-based modelling can be applied
    in various environments, but is not good at scanning objects
    with unmarked surfaces, nor is it suited for modelling
    automatically.
   3DLS has been used because of the necessity of high
    accuracy in this study.

                                                 ©2011 Tomohiro Fukuda, Osaka-U
1. Introduction -2
   It is necessary to convert the point cloud data acquired with
    3DLS into the polygon to use it to design in 3DCAD, BIM
    and VR. Using polygons is more advantageous for texture
    mapping and shadow-casting than using point cloud data.
   When the point cloud data is converted into polygons, it is
    an accumulation of small polygons.
   When an object or space is a closed flat plane,
    • it is necessary to merge the small polygons to reduce the volume of
      data, and to convert them into one polygon.
    • each normal vector of small polygons theoretically has the same angle.
   However, if the angles are not the same, it is necessary to
    clarify the variation of the angle of small polygons that
    should become one polygon.
   The purpose of this study is to clarify the variation of the
    angle of a small polygon group that should become one
    polygon based on actual data.

                                                         ©2011 Tomohiro Fukuda, Osaka-U
Contents
1. Introduction
2. 3D data modelling flow by 3DLS and this study
   1. 3D data modelling flow by using Poly-Opt
   2. Use of Poly-Opt in this study
3. Experimentation
   1. Experimental methodology
   2. Result and Discussion
4. Comparison measurement by using VR application
5. Conclusion




                                        ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
 In the previous research, three problems in a 3D data
  modelling flow from objects such as SCMOD to digital data
  by using 3DLS were pointed out.
  1. There were far more polygons and vertexes than needed. Therefore, it
     was difficult to draw shapes and to influence the rendering speed of
     3DCAD, BIM and VR.
  2. Scanned data was output as mesh data, and the shape of the edge of
     the SCMOD resembled a staircase. Therefore, there was a problem that
     the edge of the SCMOD was not expressed accurately.
  3. Part of the scanned data was lacking. This was because it is impossible
     to scan parts where the laser could not penetrate or where the CCD
     (Charge Coupled Device) was invisible.


 Thus, a system for
  resolving these
  problems, "Poly-
  Opt" was developed.



                                                         ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

The object is
scanned by using
3DLS. 3DLS cannot
scan a hidden
surface. Therefore,
the object must be
rotated in steps and
must be scanned
from 360 degrees.




                                                 ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

A set of the scanned
data is merged on
"Polygon Editing
Tool Ver.2.3" which
is the software
accompanying 3DLS.




                                                 ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

The merged
scanned data is
converted into
optimal scanned
data by using Poly-
Opt which is a
polygon
optimization system.




                                                 ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

                                           Transforming
                                           coordinate axis step:
                                           to carry out the VR
                                           authoring easily, the
                                           rotation transformation
                                           is done from the
                                           original coordinate axis
                                           of scanned data to the
                                           horizontal and vertical
                                           coordinate axis.




                                           ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

                                           Calculating planes
                                           step:
                                           the planes including
                                           each surface of
                                           scanned data are
                                           generated.




                                           ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

                                           Calculating new
                                           vertexes step:
                                           the vertex that
                                           becomes the edge of
                                           the object is calculated
                                           by making a
                                           simultaneous equation
                                           for each generated
                                           plane and obtaining
                                           the solution.




                                           ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

                                           Generating a polygon
                                           step:
                                           the polygon is
                                           generated by uniting
                                           the obtained vertex
                                           groups by the
                                           Delaunay triangulation
                                           method.




                                           ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-1. 3D data modelling flow by using Poly-Opt

                                           This data is output by
                                           a general VRML97
                                           (Virtual Reality Modelling
                                           Language)format and
                                           optimal scanned data
                                           is created.




                                           ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-2. Use of Poly-Opt in this study

                               Argument
                               • k: Index of arbitrary vector
           k
                               • θ (degree): Permissible angle of extracted
                                 normal vector
                         L1    • L1 (mm): Distance in the perpendicular
                                 direction between planes that are
                         L2      separated
                               • L2 (mm): Distance in the horizontal
                                 direction between planes that are
                                 separated
                     R
                               • R (mm): Composure given to co-domain
                                 obtained by the plane equation.




                                                   ©2011 Tomohiro Fukuda, Osaka-U
2. 3D data modelling flow by 3DLS and this study
2-2. Use of Poly-Opt in this study

   In this study, a closed flat plane from the small polygons
    converted from the point cloud data scanned by 3DLS is
    generated, and the variation of the angle of small polygons
    is investigated.
   Therefore, the normal vector extraction function which
    belongs to the calculating planes step of Poly-Opt is used.




       Example of numbers of extracted polygons per each assigned θ


                                                       ©2011 Tomohiro Fukuda, Osaka-U
Contents
1. Introduction
2. 3D data modelling flow by 3DLS and this study
   1. 3D data modelling flow by using Poly-Opt
   2. Use of Poly-Opt in this study
3. Experimentation
   1. Experimental methodology
   2. Result and Discussion
4. Comparison measurement by using VR application
5. Conclusion




                                        ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-1. Experimental methodology
   The experiment object is a hexahedron made of polystyrene,
    which has closed planes.
    • One ridge line: about 49mm
    • number of polygons: 22,217
    • number of vertexes: 11,111 scanned by using 3DLS.
   The scanning interval: 0.053mm




                                  Experimental screen shot using
                                  Poly-Opt
                                               ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-1. Experimental methodology
   The procedure of the experiment is
    1.   Thirty polygons (PolygonMn. M is surface ID, M=0-5. n is the number
         of polygon, n=30) which should be on the same plane of each surface
         (surfaceM) are selected on the interface of Poly-Opt randomly.
    2.   The extracted permissible angle θ is set on the interface of Poly-Opt.
         The number of polygons extracted within the assigned θ angle is
         counted (PolygonMnθ). θ are 17 patterns: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9,
         10, 15, 20, 25, 30, 35, and 40-degrees.
    3.   The average value (PolygonMaveθ) and the standard deviation
         (PolygonMstdevθ) of PolygonMnθ are calculated.

                                                                            (1)



                                                                            (2)



    4.   In this experiment, PolygonMaveθ and PolygonMstdevθ of thirty
         polygons (n=30) for each θ of surfaceM are calculated in a statistical
         approach.

                                                            ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-2. Result and Discussion
When θ is 0, most
numbers of extracted
polygons are 0.
That is, when θ is 0,
other polygons are
hardly extracted.
Therefore, each normal
vector of small polygons                  Average numbers
which lie in the same                     of extracted
plane is not actually the                 polygons by each
                                          acceptable angle
same angle.                               θ




                                               Standard
                                               deviation of
                                               extracted
                                               polygons
                                               by
                                               acceptable
                                               angle θ
                             ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-2. Result and Discussion
When θ is 30, the
standard deviation of
the extracted number of
polygons is very small,
from 1.2 to 3.0.
Therefore, all polygons
which lie in the same
plane are extracted.
                                          Average numbers
Some surfaceM to which                    of extracted
the standard deviation is                 polygons by each
                                          acceptable angle
smaller exist when θ is
                                          θ
larger than 30. However,
polygons which do not
obviously lie in the same
plane, such as the area
of stair-like edges, are
extracted as a result of
the observation.
                                               Standard
Then, average values of                        deviation of
the number of polygons                         extracted
                                               polygons
extracted when θ is 30                         by
are used to decide the                         acceptable
standard number of                             angle θ
polygons.                    ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-2. Result and Discussion

The variation of the
value of PolygonMnθ (M
is a fixed value.) is large
when the value of
PolygonMstdevθ is large.
Moreover, the standard                             Average numbers
deviation does not have                            of extracted
a true threshold that                              polygons by each
                                                   acceptable angle
becomes a criterion. It                            θ
is a relative value.

                              large



                                                        Standard
                                                        deviation of
                                                        extracted
                                                        polygons
                                                        by
                                                        acceptable
                                                        angle θ
                                      ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-2. Result and Discussion

When the threshold of
standard deviation is
assumed to be 100, the
θ that becomes
PolygonMstdevθ < 100
becomes
                                           Average numbers
surface0 = 7,                              of extracted
                                           polygons by each
surface1 = 6,                              acceptable angle
surface2 = 4,                              θ

surface3 = 5,
surface4 = 3, and
surface5 = 5 (degrees).
That is, it is sure to
become PolygonMstdevθ
< 100 at θ ≧7.                                  Standard
                                                deviation of
                                                extracted
                          7                     polygons
                                                by
                                                acceptable
                                                angle θ
                              ©2011 Tomohiro Fukuda, Osaka-U
3. Experimentation
3-2. Result and Discussion
Moreover, the polygon
extraction rate to
PolygonMave30 is as
follows at θ = 7:
Polygon0: 93.9%,
Polygon1: 94.7%,
Polygon2: 93.4%,                             Average numbers
                                             of extracted
Polygon3: 96.4%,                             polygons by each
                                             acceptable angle
Polygon4: 93.1%,                             θ
Polygon5: 93.8%.
That is, it becomes a
high sampling fraction of
90% or more for
PolygonMaveθ30 about
all SurfaceM.
                                                  Standard
                                                  deviation of
                                                  extracted
                            7                     polygons
                                                  by
                                                  acceptable
                                                  angle θ
                                ©2011 Tomohiro Fukuda, Osaka-U
Contents
1. Introduction
2. 3D data modelling flow by 3DLS and this study
   1. 3D data modelling flow by using Poly-Opt
   2. Use of Poly-Opt in this study
3. Experimentation
   1. Experimental methodology
   2. Result and Discussion
4. Comparison measurement by using VR application
5. Conclusion




                                        ©2011 Tomohiro Fukuda, Osaka-U
4. Comparison measurement by using VR application
   The 3D model before and after converting it with Poly-Opt is
    measured with the VR application, UC-Win/Road ver.4.00.05.
   As VR contents, two types of building models are arranged
    along a road which is 2km in length.




                                               ©2011 Tomohiro Fukuda, Osaka-U
4. Comparison measurement by using VR application




       Example of numbers of extracted polygons per each assigned θ



   When the closed plane is scanned using 3DLS, a group of
    small polygons can be effectively generated to one closed
    plane by using Poly-Opt although the 3D model verified in
    this study is only a simple hexahedron.
                                                       ©2011 Tomohiro Fukuda, Osaka-U
Contents
1. Introduction
2. 3D data modelling flow by 3DLS and this study
   1. 3D data modelling flow by using Poly-Opt
   2. Use of Poly-Opt in this study
3. Experimentation
   1. Experimental methodology
   2. Result and Discussion
4. Comparison measurement by using VR application
5. Conclusion




                                        ©2011 Tomohiro Fukuda, Osaka-U
5. Conclusion
 There are no small polygons converted by the point cloud
  data scanned by using 3DLS even if the group of small
  polygons is a closed flat plane which lies in the same plane.
  When the standard deviation of the extracted number of
  polygons is assumed to be less than 100, the variation of the
  angle of the normal vector is roughly 7 degrees.
 The standard deviation of the number of polygons extracted
  when θ is 30 is small, from 1.2 to 3, as a result of examining
  the average value and the standard deviation of the number
  of polygons extracted within the assigned θ angle by using
  Poly-Opt. Polygons which do not obviously lie in the same
  plane, such as the area of stair-like edges, are extracted
  when θ becomes larger than 30. That is, all polygons thought
  to lie in the same plane are extracted when θ is 30 degrees.
 An architect and engineer can convert a large amount of
  point cloud data into a polygon by inputting the parameter
  clarified in this study. Handling the polygon is easy in CAD,
  BIM and VR.
                                               ©2011 Tomohiro Fukuda, Osaka-U
5. Conclusion
 As a next step, it applies to a SCMOD of a building and city
  and an actual buildings though this study targets only a cube.
 Moreover, the variation of the normal vector of polygons is
  studied by using the average value and standard deviation in
  this study. Future work should attempt to find a better
  statistical technique.




                                               ©2011 Tomohiro Fukuda, Osaka-U
Acknowledgements
• A portion of this research was done with the assistance of subject number
  217604690 of the 2010 Grant-in-Aid for Scientific Research (Japan Society for the
 Promotion of Science).


References
• Hung-Ming Cheng, Ya-Ning Yen and Wun-Bin Yang: 2009, Digital archiving in
  cultural heritage preservation, CAADRIA2009, 93-101.
• Kensuke Kitagawa, Tomohiro Fukuda, Nobuyoshi Yabuki: 2010, Optimizing
  system from 3D laser scanner data to a VR model for urban design study,
  Proceedings of the 10th International Conference on Construction Applications
  of Virtual Reality (conVR) 2010, 337-346.
• Naai-Jung Shih and Jah-Yu Lee: 2009, 3D Scans of as-built urban scenes in a
  city scale, CAADRIA2009, 297-306.
• Qi Pan, Reitmayr Gerhard and Tom Drummond: 2009, Interactive model
  reconstruction with user guidance, Proceedings of int. symposium on mixed
  and augmented reality 2009 (ISMAR´09) IEEE, 209-210.
• Sabry F. El-Hakim, J. -Angelo Beraldin, and Michel Picard: 2003, Effective 3D
  modeling of heritage sites, 4th Int. Conf. 3-D Digital Imaging and Modeling,
  Banff, Canada, pp. 302-309.
• Tatsuo Konno, Kyu Tsuji, Yutaka Shimogaki, Ryosuke Shibasaki and Dinesh
  Anadhar: 2000, A new approach to mobile mapping for automated
  reconstruction of urban 3D models, The 3rd international workshop on urban
  3D and multi-media mapping (CDROM).                      ©2011 Tomohiro Fukuda, Osaka-U

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A STUDY OF VARIATION OF NORMAL OF POLY-GONS CREATED BY POINT CLOUD DATA FOR AR-CHITECTURAL RENOVATION FIELD

  • 1. CAADRIA2011, Newcastle, Australia A STUDY OF VARIATION OF NORMAL OF POLYGONS CREATED BY POINT CLOUD DATA FOR ARCHITECTURAL RENOVATION FIELD TOMOHIRO FUKUDA, KENSUKE KITAGAWA and NOBUYOSHI YABUKI Division of Sustainable Energy and Environmental Engineering, Graduate School of Engineering, Osaka University, Japan
  • 2. Contents 1. Introduction 2. 3D data modelling flow by 3DLS and this study 1. 3D data modelling flow by using Poly-Opt 2. Use of Poly-Opt in this study 3. Experimentation 1. Experimental methodology 2. Result and Discussion 4. Comparison measurement by using VR application 5. Conclusion ©2011 Tomohiro Fukuda, Osaka-U
  • 3. Contents 1. Introduction 2. 3D data modelling flow by 3DLS and this study 1. 3D data modelling flow by using Poly-Opt 2. Use of Poly-Opt in this study 3. Experimentation 1. Experimental methodology 2. Result and Discussion 4. Comparison measurement by using VR application 5. Conclusion ©2011 Tomohiro Fukuda, Osaka-U
  • 4. 1. Introduction -1  In the architectural renovation field and the urban design field, acquiring current 3D spatial data of cities, buildings, and rooms rapidly and in detail has become indispensable. The digitalization of SCMOD is also indispensable.  Recently, researches using 3DLS (3D laser scanner) to acquire 3D space have increased in the architecture, engineering, and construction fields.  3DLS can provide accurate and complete details, but is expensive and not portable. Additionally, it is not efficient for scanning large objects.  On the other hand, image-based modelling can be applied in various environments, but is not good at scanning objects with unmarked surfaces, nor is it suited for modelling automatically.  3DLS has been used because of the necessity of high accuracy in this study. ©2011 Tomohiro Fukuda, Osaka-U
  • 5. 1. Introduction -2  It is necessary to convert the point cloud data acquired with 3DLS into the polygon to use it to design in 3DCAD, BIM and VR. Using polygons is more advantageous for texture mapping and shadow-casting than using point cloud data.  When the point cloud data is converted into polygons, it is an accumulation of small polygons.  When an object or space is a closed flat plane, • it is necessary to merge the small polygons to reduce the volume of data, and to convert them into one polygon. • each normal vector of small polygons theoretically has the same angle.  However, if the angles are not the same, it is necessary to clarify the variation of the angle of small polygons that should become one polygon.  The purpose of this study is to clarify the variation of the angle of a small polygon group that should become one polygon based on actual data. ©2011 Tomohiro Fukuda, Osaka-U
  • 6. Contents 1. Introduction 2. 3D data modelling flow by 3DLS and this study 1. 3D data modelling flow by using Poly-Opt 2. Use of Poly-Opt in this study 3. Experimentation 1. Experimental methodology 2. Result and Discussion 4. Comparison measurement by using VR application 5. Conclusion ©2011 Tomohiro Fukuda, Osaka-U
  • 7. 2. 3D data modelling flow by 3DLS and this study  In the previous research, three problems in a 3D data modelling flow from objects such as SCMOD to digital data by using 3DLS were pointed out. 1. There were far more polygons and vertexes than needed. Therefore, it was difficult to draw shapes and to influence the rendering speed of 3DCAD, BIM and VR. 2. Scanned data was output as mesh data, and the shape of the edge of the SCMOD resembled a staircase. Therefore, there was a problem that the edge of the SCMOD was not expressed accurately. 3. Part of the scanned data was lacking. This was because it is impossible to scan parts where the laser could not penetrate or where the CCD (Charge Coupled Device) was invisible.  Thus, a system for resolving these problems, "Poly- Opt" was developed. ©2011 Tomohiro Fukuda, Osaka-U
  • 8. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt The object is scanned by using 3DLS. 3DLS cannot scan a hidden surface. Therefore, the object must be rotated in steps and must be scanned from 360 degrees. ©2011 Tomohiro Fukuda, Osaka-U
  • 9. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt A set of the scanned data is merged on "Polygon Editing Tool Ver.2.3" which is the software accompanying 3DLS. ©2011 Tomohiro Fukuda, Osaka-U
  • 10. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt The merged scanned data is converted into optimal scanned data by using Poly- Opt which is a polygon optimization system. ©2011 Tomohiro Fukuda, Osaka-U
  • 11. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt Transforming coordinate axis step: to carry out the VR authoring easily, the rotation transformation is done from the original coordinate axis of scanned data to the horizontal and vertical coordinate axis. ©2011 Tomohiro Fukuda, Osaka-U
  • 12. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt Calculating planes step: the planes including each surface of scanned data are generated. ©2011 Tomohiro Fukuda, Osaka-U
  • 13. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt Calculating new vertexes step: the vertex that becomes the edge of the object is calculated by making a simultaneous equation for each generated plane and obtaining the solution. ©2011 Tomohiro Fukuda, Osaka-U
  • 14. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt Generating a polygon step: the polygon is generated by uniting the obtained vertex groups by the Delaunay triangulation method. ©2011 Tomohiro Fukuda, Osaka-U
  • 15. 2. 3D data modelling flow by 3DLS and this study 2-1. 3D data modelling flow by using Poly-Opt This data is output by a general VRML97 (Virtual Reality Modelling Language)format and optimal scanned data is created. ©2011 Tomohiro Fukuda, Osaka-U
  • 16. 2. 3D data modelling flow by 3DLS and this study 2-2. Use of Poly-Opt in this study  Argument • k: Index of arbitrary vector k • θ (degree): Permissible angle of extracted normal vector L1 • L1 (mm): Distance in the perpendicular direction between planes that are L2 separated • L2 (mm): Distance in the horizontal direction between planes that are separated R • R (mm): Composure given to co-domain obtained by the plane equation. ©2011 Tomohiro Fukuda, Osaka-U
  • 17. 2. 3D data modelling flow by 3DLS and this study 2-2. Use of Poly-Opt in this study  In this study, a closed flat plane from the small polygons converted from the point cloud data scanned by 3DLS is generated, and the variation of the angle of small polygons is investigated.  Therefore, the normal vector extraction function which belongs to the calculating planes step of Poly-Opt is used. Example of numbers of extracted polygons per each assigned θ ©2011 Tomohiro Fukuda, Osaka-U
  • 18. Contents 1. Introduction 2. 3D data modelling flow by 3DLS and this study 1. 3D data modelling flow by using Poly-Opt 2. Use of Poly-Opt in this study 3. Experimentation 1. Experimental methodology 2. Result and Discussion 4. Comparison measurement by using VR application 5. Conclusion ©2011 Tomohiro Fukuda, Osaka-U
  • 19. 3. Experimentation 3-1. Experimental methodology  The experiment object is a hexahedron made of polystyrene, which has closed planes. • One ridge line: about 49mm • number of polygons: 22,217 • number of vertexes: 11,111 scanned by using 3DLS.  The scanning interval: 0.053mm Experimental screen shot using Poly-Opt ©2011 Tomohiro Fukuda, Osaka-U
  • 20. 3. Experimentation 3-1. Experimental methodology  The procedure of the experiment is 1. Thirty polygons (PolygonMn. M is surface ID, M=0-5. n is the number of polygon, n=30) which should be on the same plane of each surface (surfaceM) are selected on the interface of Poly-Opt randomly. 2. The extracted permissible angle θ is set on the interface of Poly-Opt. The number of polygons extracted within the assigned θ angle is counted (PolygonMnθ). θ are 17 patterns: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, and 40-degrees. 3. The average value (PolygonMaveθ) and the standard deviation (PolygonMstdevθ) of PolygonMnθ are calculated. (1) (2) 4. In this experiment, PolygonMaveθ and PolygonMstdevθ of thirty polygons (n=30) for each θ of surfaceM are calculated in a statistical approach. ©2011 Tomohiro Fukuda, Osaka-U
  • 21. 3. Experimentation 3-2. Result and Discussion When θ is 0, most numbers of extracted polygons are 0. That is, when θ is 0, other polygons are hardly extracted. Therefore, each normal vector of small polygons Average numbers which lie in the same of extracted plane is not actually the polygons by each acceptable angle same angle. θ Standard deviation of extracted polygons by acceptable angle θ ©2011 Tomohiro Fukuda, Osaka-U
  • 22. 3. Experimentation 3-2. Result and Discussion When θ is 30, the standard deviation of the extracted number of polygons is very small, from 1.2 to 3.0. Therefore, all polygons which lie in the same plane are extracted. Average numbers Some surfaceM to which of extracted the standard deviation is polygons by each acceptable angle smaller exist when θ is θ larger than 30. However, polygons which do not obviously lie in the same plane, such as the area of stair-like edges, are extracted as a result of the observation. Standard Then, average values of deviation of the number of polygons extracted polygons extracted when θ is 30 by are used to decide the acceptable standard number of angle θ polygons. ©2011 Tomohiro Fukuda, Osaka-U
  • 23. 3. Experimentation 3-2. Result and Discussion The variation of the value of PolygonMnθ (M is a fixed value.) is large when the value of PolygonMstdevθ is large. Moreover, the standard Average numbers deviation does not have of extracted a true threshold that polygons by each acceptable angle becomes a criterion. It θ is a relative value. large Standard deviation of extracted polygons by acceptable angle θ ©2011 Tomohiro Fukuda, Osaka-U
  • 24. 3. Experimentation 3-2. Result and Discussion When the threshold of standard deviation is assumed to be 100, the θ that becomes PolygonMstdevθ < 100 becomes Average numbers surface0 = 7, of extracted polygons by each surface1 = 6, acceptable angle surface2 = 4, θ surface3 = 5, surface4 = 3, and surface5 = 5 (degrees). That is, it is sure to become PolygonMstdevθ < 100 at θ ≧7. Standard deviation of extracted 7 polygons by acceptable angle θ ©2011 Tomohiro Fukuda, Osaka-U
  • 25. 3. Experimentation 3-2. Result and Discussion Moreover, the polygon extraction rate to PolygonMave30 is as follows at θ = 7: Polygon0: 93.9%, Polygon1: 94.7%, Polygon2: 93.4%, Average numbers of extracted Polygon3: 96.4%, polygons by each acceptable angle Polygon4: 93.1%, θ Polygon5: 93.8%. That is, it becomes a high sampling fraction of 90% or more for PolygonMaveθ30 about all SurfaceM. Standard deviation of extracted 7 polygons by acceptable angle θ ©2011 Tomohiro Fukuda, Osaka-U
  • 26. Contents 1. Introduction 2. 3D data modelling flow by 3DLS and this study 1. 3D data modelling flow by using Poly-Opt 2. Use of Poly-Opt in this study 3. Experimentation 1. Experimental methodology 2. Result and Discussion 4. Comparison measurement by using VR application 5. Conclusion ©2011 Tomohiro Fukuda, Osaka-U
  • 27. 4. Comparison measurement by using VR application  The 3D model before and after converting it with Poly-Opt is measured with the VR application, UC-Win/Road ver.4.00.05.  As VR contents, two types of building models are arranged along a road which is 2km in length. ©2011 Tomohiro Fukuda, Osaka-U
  • 28. 4. Comparison measurement by using VR application Example of numbers of extracted polygons per each assigned θ  When the closed plane is scanned using 3DLS, a group of small polygons can be effectively generated to one closed plane by using Poly-Opt although the 3D model verified in this study is only a simple hexahedron. ©2011 Tomohiro Fukuda, Osaka-U
  • 29. Contents 1. Introduction 2. 3D data modelling flow by 3DLS and this study 1. 3D data modelling flow by using Poly-Opt 2. Use of Poly-Opt in this study 3. Experimentation 1. Experimental methodology 2. Result and Discussion 4. Comparison measurement by using VR application 5. Conclusion ©2011 Tomohiro Fukuda, Osaka-U
  • 30. 5. Conclusion  There are no small polygons converted by the point cloud data scanned by using 3DLS even if the group of small polygons is a closed flat plane which lies in the same plane. When the standard deviation of the extracted number of polygons is assumed to be less than 100, the variation of the angle of the normal vector is roughly 7 degrees.  The standard deviation of the number of polygons extracted when θ is 30 is small, from 1.2 to 3, as a result of examining the average value and the standard deviation of the number of polygons extracted within the assigned θ angle by using Poly-Opt. Polygons which do not obviously lie in the same plane, such as the area of stair-like edges, are extracted when θ becomes larger than 30. That is, all polygons thought to lie in the same plane are extracted when θ is 30 degrees.  An architect and engineer can convert a large amount of point cloud data into a polygon by inputting the parameter clarified in this study. Handling the polygon is easy in CAD, BIM and VR. ©2011 Tomohiro Fukuda, Osaka-U
  • 31. 5. Conclusion  As a next step, it applies to a SCMOD of a building and city and an actual buildings though this study targets only a cube.  Moreover, the variation of the normal vector of polygons is studied by using the average value and standard deviation in this study. Future work should attempt to find a better statistical technique. ©2011 Tomohiro Fukuda, Osaka-U
  • 32. Acknowledgements • A portion of this research was done with the assistance of subject number 217604690 of the 2010 Grant-in-Aid for Scientific Research (Japan Society for the Promotion of Science). References • Hung-Ming Cheng, Ya-Ning Yen and Wun-Bin Yang: 2009, Digital archiving in cultural heritage preservation, CAADRIA2009, 93-101. • Kensuke Kitagawa, Tomohiro Fukuda, Nobuyoshi Yabuki: 2010, Optimizing system from 3D laser scanner data to a VR model for urban design study, Proceedings of the 10th International Conference on Construction Applications of Virtual Reality (conVR) 2010, 337-346. • Naai-Jung Shih and Jah-Yu Lee: 2009, 3D Scans of as-built urban scenes in a city scale, CAADRIA2009, 297-306. • Qi Pan, Reitmayr Gerhard and Tom Drummond: 2009, Interactive model reconstruction with user guidance, Proceedings of int. symposium on mixed and augmented reality 2009 (ISMAR´09) IEEE, 209-210. • Sabry F. El-Hakim, J. -Angelo Beraldin, and Michel Picard: 2003, Effective 3D modeling of heritage sites, 4th Int. Conf. 3-D Digital Imaging and Modeling, Banff, Canada, pp. 302-309. • Tatsuo Konno, Kyu Tsuji, Yutaka Shimogaki, Ryosuke Shibasaki and Dinesh Anadhar: 2000, A new approach to mobile mapping for automated reconstruction of urban 3D models, The 3rd international workshop on urban 3D and multi-media mapping (CDROM). ©2011 Tomohiro Fukuda, Osaka-U