The document discusses technologies for cultural heritage applications, focusing on accurate digitization of sites and artifacts. It describes how modern sensors produce large amounts of data that are difficult to process, store, distribute and visualize at scale. Research is needed into scalable enabling technologies for acquisition, geometric processing, visualization and more. The goal is to efficiently acquire and process 3D models' geometry and color from sites and objects, and to effectively archive, distribute and visualize the resulting models.

1. www.crs4.it/vic/
Tecnologie di Visual Computing
per i Beni Culturali
R. Pintus
CRS4 Visual Computing

2. R. Pintus – CRS4/ViC, October 2012
Tecnologie per i beni culturali
• Focus: digitalizzazione accurata (forma e
colore) di siti e manufatti + …
– Partire dai dati: Acquisizione -> Trattamento !
– Modelli misurabili
• Molti usi oltre la visualizzazione
– Riproduzione materica
– Studio di opere d’arte
– Documentazione in-situ di scavi archeologici
– Supporto al restauro e sua documentazione
– Valorizzazione

3. R. Pintus – CRS4/ViC, October 2012
Tecnologie per i beni culturali
• Le quantità di dati prodotte dai moderni sensori
sono però difficili da trattare, archiviare,
distribuire, visualizzare
– Scalabilità!
• Tecniche attuali sub-ottimali
– Costi, tempi, qualità
• Bisogno di ricerca in tecnologie abilitanti
scalabili
– Acquisizione
– Processamento geometrico
– Visualizzazione
– …

4. R. Pintus – CRS4/ViC, October 2012
Tecnologie per i beni culturali
• Come acquisire e processare efficacemente forma e colore di
siti e manufatti?
– Tecniche di fusione multi-sensore, stream-processing, multiresolution, external
memory algorithms, parallel programming, GPGPUs
• Come archiviare e distribuire efficacemente i modelli?
– Multiresolution, adaptive streaming, compression
• Come visualizzarli efficacemente?
– Multiresolution, adaptive rendering, out-of-core methods, GPU programming,
parallelization, rasterization, ray-casting
• Come esplorarli?
– Novel 3D displays, specific interaction techniques
– Portable devices

5. R. Pintus – CRS4/ViC, October 2012
Alcuni esempi
• Allineamento geometria/colore
• Colorazione di modelli 3D
• Fusione di dati e ricostruzione geometrica
• Visualizzazione scalabile ed interattiva
• Distribuzione di dati in rete
• Esplorazione su display innovativi
(… e molto altro)

6. R. Pintus – CRS4/ViC, October 2012
Our Goal
6

7. R. Pintus – CRS4/ViC, October 2012
Modelling vs Acquisition
Modelling
Subjective Reality
Acquisition
Objective reality
7

8. R. Pintus – CRS4/ViC, October 2012
3D Reconstruction
• Acquire geometry and color
• A lot of techniques
– Structured light, laser scanning (triangulation or
time-of-flight), photometric stereo, shape-from-X,
…
• Which technique?
– Object type (big/small, material….)
– Cost
– Accuracy/Resolution
– Time
– Complexity
8

9. R. Pintus – CRS4/ViC, October 2012
Outline
• 3D Reconstruction Techniques
• 3D Reconstruction Pipeline
– Photo mapping/blending
– Printing
• Case study
9

10. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
10

11. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring
machines (CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
11

12. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
12

13. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Stereo
13

14. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Stereo
14

15. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
15

16. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Multiview
16

17. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Multiview
17

18. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
18

19. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Silhouettes
19

20. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
20

21. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Depth from
focus/defocus
21

22. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
22

23. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Transmissive
Computed Tomography
Density Function
Trasmissive Ultrasound
23

24. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
24

25. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Non-Optical
Non-
Ultrasound Radar MRI
25

26. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
26

27. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Time-of-Flight
Time-of-
2d 5.0m
t= = ≈ 17ns
c 3 × 108 m s
27

28. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
28

29. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Laser Striping
29

30. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
30

31. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Structured Lighting
31

32. R. Pintus – CRS4/ViC, October 2012
Taxonomy (non-destructive)
(non-
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
32

33. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Photometric Stereo
33

34. R. Pintus – CRS4/ViC, October 2012
Photometric Stereo – SEM
34

35. R. Pintus – CRS4/ViC, October 2012
Taxonomy – Photometric Stereo
35

36. R. Pintus – CRS4/ViC, October 2012
Taxonomy SEM
• Contact
– Direct Measurements
• rulers, calipers, pantographs, coordinate measuring machines
(CMM), AFM
• Non-Contact
– Passive – Active
• Shape-from-X • Transmissive
– Stereo – Computed Tomography (CT)
– Multiview – Transmissive Ultrasound
– Silhouettes • Reflective
– Focus/Defocus – Non-Optical Methods
» reflective ultrasound, radar, sonar, MRI
– Time-of-Flight
– Triangulation
» laser striping
» structured lighting
– Photometric Stereo
36

37. R. Pintus – CRS4/ViC, October 2012
Cultural Heritage
• Techniques
– Triangulation (laser scanner)
– Time of Flight
– Texture Mapping
– Multi-view reconstruction
– Photometric Stereo
• Deal with multiple acquisitions
• Manage a huge amount of data for visualization
purposes
37

38. R. Pintus – CRS4/ViC, October 2012
3D Reconstruction Pipeline
Real Object Acquisition Devices
Photos
3D Digital Model Geometry
=== Processing ===
-Cleaning
- Merging
- Photo Alignment
- Color Projection
-…
38

39. R. Pintus – CRS4/ViC, October 2012
3D Reconstruction Pipeline
• Real Model Inspection (onsite)
• Scans design (offsite/onsite)
• Acquisition (onsite)
• Alignment (offsite)
• Editing (offsite)
• Merge (offsite)
• Texture (offsite)
• Final Model (offsite)
• 3D Printing (offsite)
39

40. R. Pintus – CRS4/ViC, October 2012
3D Reconstruction Pipeline
• Real Model Inspection (onsite)
• Scans design (offsite/onsite)
• Acquisition (onsite)
• Alignment (offsite)
• Editing (offsite)
• Merge (offsite)
• Texture (offsite)
• Final Model (offsite)
• 3D Printing (offsite)
40

41. R. Pintus – CRS4/ViC, October 2012
Goal
• Fast and low-cost technique for creating
accurate colored models
• Acquisition
– 3D – laser scanners
– Color – digital cameras
• Mapping photo-to-geometry
– Fast and Robust Semi-Automatic Registration of Photographs
to 3D Geometry
• Photo blending
– A Streaming Framework for Seamless Detailed Photo
Blending on Massive Point Clouds

42. www.crs4.it/vic/
Photo Mapping
Ruggero Pintus, Enrico Gobbetti, and Roberto Combet. “Fast and Robust Semi-Automatic
Registration of Photographs to 3D Geometry”. In The 12th International Symposium on Virtual
Reality, Archaeology and Cultural Heritage, October 2011.

43. R. Pintus – CRS4/ViC, October 2012
Problem Statement
3D Geometry Unordered Set
Of n Uncalibrated
Photos
n Camera Poses
(2D/3D Registration)

44. R. Pintus – CRS4/ViC, October 2012
Related work
• Manual selection of 2D-3D matches
– Massive user intervention – Tiring and time-consuming
• Automatic feature matching
– Not robust enough for a generic dataset
• Semi-automatic statistical correlation
– Point cloud attributes not always provided
• Geometric multi-view reconstruction
– 2D-3D problem 3D-3D registration task
– dense and ordered frame sequence
• Our contribution
– Minimize user intervention / Large datasets / Semi-
automatic / Multi-view based approach / No Attributes

45. R. Pintus – CRS4/ViC, October 2012
Input Data
User Dense 3D n Photos • Dense Geometry
– Point cloud, triangle
mesh, etc.
SfM Reconstruction
– No attributes
– No particular features
Coarse Registration • n photos
– Naïve constraints:
Refinement • Blur, Noise, Under- or
over-exposured
– Sufficient overlap
Output Data

46. R. Pintus – CRS4/ViC, October 2012
Multi-
Multi-view
User Dense 3D n Photos • Bundler [Snavely et al.
2006]
– SfM system for unordered
SfM Reconstruction image collections
– http://phototour.cs.washingto
n.edu/bundler/
Coarse Registration • Output
– A sparse point cloud
– n camera poses
Refinement – SIFT keypoints (projections of
sparse 3D points)
Output Data

47. R. Pintus – CRS4/ViC, October 2012
Coarse registration
User Dense 3D n Photos • Register two point clouds
with different:
– scales
– reference frames
SfM Reconstruction – resolutions
• Automatic methods are not
robust and efficient enough
Coarse Registration • User aligns few images (one
or more) to the dense
geometry
Refinement • Affine transformation is
applied to all cameras and
sparse points
Output Data

48. R. Pintus – CRS4/ViC, October 2012
Refinement
User Dense 3D n Photos Pj
C1
Q (C2 , p j ) s1, j
pj
SfM Reconstruction
s2 , j NN F ( p j )
Coarse Registration
Q (C2 , NN F ( p j ))
Refinement C2
E (C , P ) = ∑∑ vij Q (Ci , NN F ( p j )) − si , j
N P NC
2
Output Data j =1 i =1

49. R. Pintus – CRS4/ViC, October 2012
Refinement
User Dense 3D n Photos • Sparse Bundle
Adjustment (SBA)
– Constants – SIFT keypoints,
SfM Reconstruction dense 3D points
– Variables – Camera poses,
sparse 3D points
Coarse Registration – SBA
• A Generic SBA C/C++ Package
Based on the Levenberg-
Marquardt Algorithm
Refinement • http://www.ics.forth.gr/~loura
kis/sba/
Output Data

50. R. Pintus – CRS4/ViC, October 2012
Output data
User Dense 3D n Photos • n camera poses
SfM Reconstruction • Input of photo
blending
– n photos
Coarse Registration
– n camera poses
– Dense 3D geometry
Refinement
Output Data

51. R. Pintus – CRS4/ViC, October 2012
Results – Photo mapping

52. www.crs4.it/vic/
Photo Blending
Ruggero Pintus, Enrico Gobbetti, and Marco Callieri. A Streaming Framework for Seamless Detailed
Photo Blending on Massive Point Clouds. In Proc. Eurographics Area Papers. Pages 25- 32, 2011.

53. R. Pintus – CRS4/ViC, October 2012
Problem Statement
Point Cloud Calibrated
Photos

54. R. Pintus – CRS4/ViC, October 2012
Problem Statement
Point Cloud Calibrated
Photos
P

55. R. Pintus – CRS4/ViC, October 2012
Problem Statement
Calibrated Colored
Point Cloud
Photos Point Cloud
P

56. R. Pintus – CRS4/ViC, October 2012
Problem Statement
Calibrated Colored
Point Cloud
Photos Point Cloud
P
• Problem Unlimited size of 3D model (Gpoints) and unlimited
number of images

57. R. Pintus – CRS4/ViC, October 2012
Related work
• State-of-the-art techniques
– Image quality estimation
– Stitching or blending
• Data representation
– Triangle meshes – exploit connectivity
– Meshless approaches
• Both triangle meshes and point clouds
• Memory settings
– All in-core – no massive geometry/images
– 3D in-core and images out-of-core – no massive geometry
– All out-of-core – Low performances
• Our contribution
– Blending function / Streaming framework / Massive point cloud /
Adaptive geometry refinement

58. R. Pintus – CRS4/ViC, October 2012
Pipeline
Masked
Per-pixel
Photo Stencil Per-pixel
Weight
Weight

59. R. Pintus – CRS4/ViC, October 2012
Simple blending

60. R. Pintus – CRS4/ViC, October 2012
Edge extraction and Distance
Transform

61. R. Pintus – CRS4/ViC, October 2012
Smooth weight

62. R. Pintus – CRS4/ViC, October 2012
Smooth weight

63. R. Pintus – CRS4/ViC, October 2012
Single band blending

64. R. Pintus – CRS4/ViC, October 2012
Multi band blending

65. R. Pintus – CRS4/ViC, October 2012
Adaptive point refinement

66. R. Pintus – CRS4/ViC, October 2012
Adaptive point refinement

67. R. Pintus – CRS4/ViC, October 2012
Adaptive point refinement

68. R. Pintus – CRS4/ViC, October 2012
Adaptive point refinement

69. R. Pintus – CRS4/ViC, October 2012
Results
David • Callieri et. al 2008 – David
28M
470Mpoints – Disk space occupancy –
6.2GB
– Computation time – 15.5
hours

70. R. Pintus – CRS4/ViC, October 2012
Results – Church’s Apse
14 Mpoint Geometry 40 photos

71. R. Pintus – CRS4/ViC, October 2012
Results – Church’s Apse

72. R. Pintus – CRS4/ViC, October 2012
Results – Grave
21 photos
8 Mpoint Geometry

73. R. Pintus – CRS4/ViC, October 2012
Results – Grave

74. R. Pintus – CRS4/ViC, October 2012
Results

75. R. Pintus – CRS4/ViC, October 2012
Results

76. R. Pintus – CRS4/ViC, October 2012
Results
David
470Mpoints
Image size – 19456x53248
1Gpixel

77. R. Pintus – CRS4/ViC, October 2012
Results

78. R. Pintus – CRS4/ViC, October 2012
Conclusion
• Image-to-geometry registration approach
• Minimum user intervention
• No constraints on geometry, attributes and features
• Specific robust cost function and SBA
• Out-of-core photo blending approach (Point clouds of unlimited size)
• Incremental color accumulation (Unlimited number of images)
• Smooth weight function (Seamless color blending)
• Streaming framework (Performance improvement)
• Adaptive point refinement
• Future work
– Automatic sparse-to-dense geometry registration
– Interactive blending - adding and removing images in an interactive tool
– Fast visual check of previous alignment step

79. R. Pintus – CRS4/ViC, October 2012
Conclusion
• Low cost
– Personal computer
– Digital camera
– Decreased manual intervention
• Open Source / Free Software
– Bundler – SfM reconstruction –
http://phototour.cs.washington.edu/bundler/
– Sparse Bundle Adjustment – SBA – Minimization –
http://www.ics.forth.gr/~lourakis/sba/
– Opengl / GLSL shaders – Rendering – http://www.opengl.org/
– Qt – Interface – http://qt.nokia.com/
– Opencv – Manual registration – http://opencv.willowgarage.com/wiki/
– Spaceland Library – Geometric computation –
http://spacelib.sourceforge.net/
– IIPImage – Web-based Viewer – http://iipimage.sourceforge.net/

80. R. Pintus – CRS4/ViC, October 2012
3D Printing
80

81. R. Pintus – CRS4/ViC, October 2012
Printing Process
• Original model
• Slice
representation
• Layer by layer
deposition
• Cleaning
• Printed model
81

82. R. Pintus – CRS4/ViC, October 2012
Printing Process
• Original model
• Slice
representation
• Layer by layer
deposition
• Cleaning
• Printed model
82

83. R. Pintus – CRS4/ViC, October 2012
Printing Process
• Original model
• Slice
representation
• Layer by layer
deposition
• Cleaning
• Printed model
83

84. R. Pintus – CRS4/ViC, October 2012
Printing Process
• Original model
• Slice
representation
• Layer by layer
deposition
• Cleaning
• Printed model
84

85. R. Pintus – CRS4/ViC, October 2012
Printing Process
• Original model
• Slice
representation
• Layer by layer
deposition
• Cleaning
• Printed model
85

86. R. Pintus – CRS4/ViC, October 2012
Geometry processing
86

87. R. Pintus – CRS4/ViC, October 2012
Geometry processing
87

88. R. Pintus – CRS4/ViC, October 2012
Geometry processing
88

89. R. Pintus – CRS4/ViC, October 2012
Sub-
Sub-surface scattering
89

90. R. Pintus – CRS4/ViC, October 2012
Color enhancement
90

91. R. Pintus – CRS4/ViC, October 2012
Color enhancement
91

92. R. Pintus – CRS4/ViC, October 2012
Color enhancement
92

93. R. Pintus – CRS4/ViC, October 2012
Conclusioni
• Lavorare su dati misurati è un pre-
requisito di molti lavori (tutti?) nel
contesto dei beni culturali
– Applicazioni specialistiche o per grande pubblico
• Le moderne tecnologie di acquisizione
consentono di acquisire una grande
quantità di informazioni (forma e colore)
– Laser scanning, camere digitali, ecc.
• Uso potenziale vasto!
– Valorizzazione, restauro, studio, ecc.

94. R. Pintus – CRS4/ViC, October 2012
Conclusioni
• Queste quantità di dati sono però difficili
da trattare, archiviare, distribuire,
visualizzare
– Scalabilità!
• Tecniche attuali sub-ottimali
– Costi, tempi, qualità

95. R. Pintus – CRS4/ViC, October 2012
Conclusioni
• Il CRS4 è impegnato in attività di ricerca
per migliorare le tecnologie…
– Stato dell’arte internazionale
– Collaborazioni e ricadute locali
• PMI, Contro Restauro SS, Soprintendenze, CNR, UniCA
• … e per applicarle a casi concreti
– Collaborazioni multidisciplinari!

96. R. Pintus – CRS4/ViC, October 2012
Conclusioni
96

97. R. Pintus – CRS4/ViC, October 2012
Questions & Contacts
• CRS4 – VIC
www.crs4.it/vic/
• Ruggero Pintus
ruggero@crs4.it