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3D Acquisition and Modeling in Cultural Heritage

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Gabriele Guidi is responsible for the “Computer Vision and Reverse Engineering Laboratory” at Politecnico di Milano (Italy). Since the late 1990s, it has focused on 3D acquisition and modeling techniques of cultural heritage artifacts on very small to very large scales. An interesting quality of a polytechnic institution like the one in Milan is to have in its DNA both a technical mind, coming from the Engineering departments, and a humanistic soul, linked to its departments of Architecture and Design. This dual point of view is critical when applying advanced technologies such as 3D data capture, opto-electronics, image processing, metrology and computer graphics to 3D documentation of a cultural artifact in a way that is useful for archaeologists, architects and officers of institutions responsible for the conservation of cultural heritage.

This presentation introduces the research group at Politecnico di Milano and presents an overview of the technological evolution of 3D capturing techniques since 2000. Several major examples of the researches done are shown, as well as how such discoveries have been applied to concrete problems of cultural heritage documentation and visualization. In the conclusion some of the major challenges we intend to confront in the near future are mentioned.

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3D Acquisition and Modeling in Cultural Heritage

  1. 1. Indiana University, Bloomington (IN) USA - Nov. 21, 2014 GABRIELE GUIDI, PHD DEPT. OF MECHANICAL ENGINEERING POLITECNICO DI MILANO, ITALY 3D ACQUISITION AND MODELING IN CULTURAL HERITAGE: EVOLUTION AND PERSPECTIVES
  2. 2. 3D FOR CULTURAL HERITAGE RESEARCH GROUP Gabriele Guidi Coordinator Electronic Engineer Michele Russo Temporary Researcher Architect Laura Micoli Post Doc Architect Davide Angheleddu Phd Student Architect Sara Gonizzi Phd Student Archaeologist + 1 to 3 intern students and 1 to 3 thesis students depending on the period
  3. 3. RESEARCH ACTIVITIES • Integration of Passive/Active technologies • Characterization of 3D acquisition technologies • 3D post processing • Applications of 3D acquisition and modeling to: • Cultural Heritage (reality based & reconstruction) • Industrial applications
  4. 4. A BIT OF MY/OUR STORY 1988! •!GG: Master degree in Electronic Engineering, Univ. Florence! •!Thesis on real-time signal processing of Doppler signals! 1992! •!GG: PhD in Bioengineering, Univ. Bologna! •!Thesis on measurement of blood speed in 3D! 1998! •!Marc Levoy scans the David by Michelangelo! 1999! •!Parnaso project! •!First experiments with 3D scanning of CH at Univ. Florence! 2000! •!First large 3D scanning project at the Univ. of Florence! •!Maddalena by Donatello!
  5. 5. MADDALENA BY DONATELLO • Sculpted in 1455 approx. • Height 180 cm • Width 40 cm • Complex shape involving shades and fragmented range maps • Wooden statue originally gold coated: currently dark with reflective spots (optically non cooperative)
  6. 6. Golden decorations (high reflectance) Areas with no decoration (high absorption)
  7. 7. EQUIPMENT USED • Generates 3D images (range maps) • Working principle: triangulation • Pattern projection of vertical strips MEA S U R ING R A N G E 0 . 5 - 1 . 2 M MEA S U R EMENT UNC E R TA INT Y 0 . 0 5 - 0 . 2 MM S E N S O R S I Z E ( P I X E L ) 7 6 8 X 5 7 6 MEA S U R EMENT R E S O LU T ION 0 . 5 - 0 . 1 MM
  8. 8. PROJECT PLANNING •! First stage: model skeleton! –! Required resolution: 0.4 mm! –! Framed field (focal plane): 30x23 cm! –! Uncertainty along z: 0.125 mm! –! Volume divided in 11 stripes 23 cm tall, vertically overlapped (29%)! –! Each stripe divided in 8-10 images (range maps) horizontally overlapped (~30%)! –! Supplemental images for hands, legs and arms! •! Second stage: Hi-res model! –! Final required resolution: 0.25 mm! –! Framed area: 19x14 cm! –! Uncertainty along z : 0.070 mm!
  9. 9. EXPERIMENTAL SET-UP Range device Range device control unit Pre-alignment work-station
  10. 10. STANDARD POST-PROCESSING Alignment of the acquired range maps! Merge in a single mesh! Editing!
  11. 11. ADDITIONAL SUB-MODELS Head! Resolution: 250 μm! !z: 70 μm! Hands! Resolution: 250 μm! z: 70 μm! Face detail! Resolution: 100 μm! !z: 21 μm! Right foot! Resolution: 100 μm! !z: 21 μm! Hands !μ
  12. 12. SUMMARY Uncertainty!70-125 "m!21 "m!21 "m! Triangles!4.6 M!1.2M!724 k! Size (Mbyte)!115.3!30.89!19.65! 2.64! Total data (Gbyte)! 374!13!23! Number of range maps! Full model!Face!Foot! Phase 1! !200 range maps, 170 used! !155 work hours! Phase 2! 205 additional range maps! 160 man-hours!
  13. 13. QUALITY CHECK • At this stage an acquisition work was usually considered completed • In our project a quality control was arranged in order to check the metric reliability of the whole model • A complementary method was used in order to achieve such purpose: photogrammetry
  14. 14. d1 – d5! Agreement between 3D and photogrammetry! d6 – d8! Positive deviations worst case: 4.3 mm (0.25 %)! d9 – d11! Negative deviations worst case : -4.2 mm (1.66 %) ! d1!d2! d8! d3! d4! d5! d6!d7! d9! d10!d11!
  15. 15. 2001! •!Visiting Researcher at NRC Canada with Angelo J. Beraldin! •!Integration of photogrammetry and 3D scanning!
  16. 16. TARGET EXTRACTION MIXING 2D AND 3D INFORMATION Geometry! Texture! xt, yt! 3D plane! Geometry Projection! x,y,z!
  17. 17. 3D model! Alignment and merging! A! B! C! D! Photogrammetric X Y Z coordinates! A! B! C! D! Roto-translation matrices! 3D images in the photogrammetric coordinate system! Quaternion! A! B! C! D!
  18. 18. FINAL CHECK • Mesurements on the new model were coherent with photogrammetry: the new model grown in height of few millimeters • By comparing the two models other lateral unexpected distortions became evident
  19. 19. LESSON LEARNED • the usual approach for creating 3D models from small range images may involve a loss of metric accuracy even when the single images are highly accurate • A sensor fusion between the two methods allowed to overcome the alignment problems • As a general criteria 3D scanning should always be coupled to a complementary measurement method at least for checking global accuracy
  20. 20. “ADORAZIONE DEI MAGI” BY LEONARDO DA VINCI Grey coded pattern projection range camera! 2002! •!First 3D acquisition and modeling of a wooden painting!
  21. 21. 3D MODEL OF THE PAINTING 393 range maps, H&V res= 0.3 mm! 222 range maps, H&V res=0.4!
  22. 22. DEVIATION FROM PLANARITY Front side Rear side
  23. 23. LESSON LEARNED • High resolution dimensional monitoring appears to be extremely useful for applications in wood restoration, specially when it is the support of a delicate painting • However, due to the natural deformations of wood, the possibility of repeating the same monitoring in different times seems a key feature for gaining the information needed by restorators
  24. 24. 2003! •!First 3D scan with a Laser Radar in the CH field! Pietà (Michelangelo) 1997, IBM Pattern projection (triangulation) Madonna col Bambino (G. Pisano) 1997, Univ. Padova / NRC Canada Laser scanning (triangulation) David (Michelangelo) 1999, Stanford University Laser scanning (triangulation) Maddalena (Donatello) 2001, Univ. Firenze, NRC Canada, Optonet Srl Pattern projection (triangulation)
  25. 25. TOF VS. TRIANGULATION (METROLOGY) Measurement uncertainty Triangulation range device 0.1 mm Time-of-flight range device 4-8 mm
  26. 26. LASER RADAR WORKING PRINCIPLE
  27. 27. METROLOGY IMPROVEMENT Measurement uncertainty Triangulation range device 0.1 mm Time-of-flight range device 4-8 mm Frequency modulated Laser Radar 0.1 mm
  28. 28. 3D DATA ALIGNMENT Triangulation based camera • Mostly local to the camera • Range maps have to be aligned by means of semi-automatic procedures (ICP) • Range maps have to be redundant in order to make ICP work FM laser radar • All 3D data are directly re-oriented in a global reference system thanks to special targets over the scene (metallic spheres)
  29. 29. MODEL GENERATION PIPELINE Triangulation sensor! 3D scanning! ICP! method! Camera referenced! Range maps! Aligned range maps (referenced to a single coordinate system)! Merge! Polygonal model! FM Laser Radar! 3D scanning! Range maps ! Referenced to a ! single coordinate system! Merge! Polygonal model!
  30. 30. DAV I D B Y DONATELLO • Height 160 cm • Located over a 1m basement Critical points • Non cooperative material • Hidden surfaces Lateral resolution needed • 1mm on low curvature surfaces • 0.5mm on compex surfaces
  31. 31. • System capable to work through a Front Surface Mirror (FSM) • From the same point of view front and rear points can be captured • 80 hours for acquisition • 20 hours for merge & preliminary editing (much less than in previous project!)
  32. 32. 6M POLYGONS FINAL MODEL
  33. 33. LESSON LEARNED • Acquisitions from a single point of view dramatically enhance the amount of surface captured in a single acquisition • The possibility to use mirrors further increases this feature, solving also problems of data alignment in objects with small thickness • Metallic rectified sphere added on the scene allow automatic 3D data orientation
  34. 34. 2003-6! •! 3D acquisition of a large and detailed object:“Plastico di Roma Antica”! •! CAD remodeling on the scanned data: “Rome Reborn”!
  35. 35. MOTIVATIONS • Digital Roman Forum project (Frischer et al. 1999-2003) • Rome Reborn project (Frischer et al. 2004-2008): extend this virtual model of ancient Rome up to the exterior walls • Idea: reverse engineering Gismondi’s “plastico” for creating a good starting point • Updated with the most recent archaeological discoveries
  36. 36. 3D DIGITIZATION CONSTRAINTS 17.4 m 16.0 m • No measurement machinery flying over the “plastico” • Long range (7-24 m) • Wide area (about 200 sq. m) • Small buildings (2-20 cm) Low uncertainty (<0.5 mm) • Balcony pavement at 2.7 meters respect to the model • Balustrade 1.2m high 1.2 m 20 cm 5 cm ! 3mm Plaster plane Balcony plane 2.7 m 24 7 Observation point Plaster plane Balcony plane
  37. 37. METRIS LASER RADAR • Known: same equipment used for the David’s work • Range = up to 24 m • Uncertainty (1σ): 300 μm (metrology mode) • Framed area: 360° H x 90° V (from -45° to +45°) • Beam spot size = 400 μm with automatic refocusing (metrology mode) • Stacking mode: reduce uncertainty averaging repeated measures (metrology mode) ➜ Metrologically Ok
  38. 38. …BUT, WHAT ABOUT SPEED? • Triangulation range device: >150 000 points/s • TOF range device: > 20 000 points/s • Laser radar in metrology mode: 1 point/s (!) ➜ time for one complete scan: 40 days (nights included). Not feasible!!
  39. 39. SYSTEM CUSTOMIZATION The most time consuming activity in metrology mode is refocusing ☟ • Scanning on circular scanlines • Focusing only once (at the beginning of each scan line) • Stacking level optimized for the best tradeoff
  40. 40. FEATURES OPTIMIZATION (mm) No averaging Average on 2 Average on 5 Average on 10 • Several averaging test were made using planar targets • Best tradeoff: average on 4 values • !=0.3 mm • Speed: 170 points/s
  41. 41. SYSTEM SETTINGS • 2 mm resolution • 0.3 mm uncertainty • 200 m2 per scan • 50 millions of points per scan • Registration with external targets, no need for redundancy ➜ Time for a complete scan of the “plastico”: 4 days (nights included). Not fast but feasible
  42. 42. TYPICAL SCANNING SESSION 1. Locate the scanner in place 2. Measure targets for determining scanner position 3. Measure the plaster perimeter from that particular location 4. Off-line calculation intersections between circular scan-lines and the perimeter ➭ pass them as input of the custom control software ➭ start scanning
  43. 43. TYPICAL SCANNING SESSION 1. Locate the scanner in place 2. Measure targets for determining scanner position 3. Measure the plaster perimeter from that particular location 4. Off-line calculation intersections between circular scan-lines and the perimeter ➭ pass them as input of the custom control software ➭ start scanning
  44. 44. TYPICAL SCANNING SESSION 1. Locate the scanner in place 2. Measure targets for determining scanner position 3. Measure the plaster perimeter from that particular location 4. Off-line calculation intersections between circular scan-lines and the perimeter ➭ pass them as input of the custom control software ➭ start scanning
  45. 45. TYPICAL SCANNING SESSION 1. Locate the scanner in place 2. Measure targets for determining scanner position 3. Measure the plaster perimeter from that particular location 4. Off-line calculation intersections between circular scan-lines and the perimeter ➭ pass them as input of the custom control software ➭ start scanning
  46. 46. PLANNING First stage • Acquisition from 3 locations on the balcony for a first massive data capture Second stage • Searching several optimal locations for small data integrations • Actual acquisition • Data merge • Editing
  47. 47. FIRST STAGE • Laser radar only • 3 locations on the balcony • Blind areas below 45° (to be integrated) • 12 days total scanning time Blind areas
  48. 48. DATA SUBDIVISION AND MESHING • Each scan 50 MPoints • huge data set, not manageable at that time (2004-5) • sets 2m x 2m blocks generated with a 3D grid • aligment made globally, integration and meshing singularly on each block
  49. 49. SECOND STA G E ( 1 ) • Laser radar for integrations of the central area • 1 more locations from the balcony (4) • 6 locations at ground level (5-10)
  50. 50. SECOND STA G E ( 2 ) • Minolta Vivid 900 sensor • Range maps all around the Aurelian Walls • Integrated with LR data through ICP alignment
  51. 51. AT THE END OF SUCH PROCESS THE WHOLE MESH WAS COMPLETED
  52. 52. R E A L V S . DIGITIZED • resolution and uncertainty chosen resulted sufficient to detect all the details • the result was significant considering the technical and logistic difficulties • however…
  53. 53. DRAWBACKS • a lot of occlusions " these nice meshes required a considerable amount of editing work • a mesh is still a mesh (e.g. e static representation of a 3D geometry) • LOD might be implemented up to acquisition resolution, while in a VR application closeups might be needed ! remodeling over the mesh Edited mesh Simplified unedited mesh
  54. 54. REMODELING THE MESH • different approaches are possible • very different in terms of time-consumption and visual result • the squared area has been processed differently in the next slides
  55. 55. JUST THE EDITED MESH
  56. 56. THE DETAILED REMODELING OF THE BUILDING (SEVERAL DAYS)
  57. 57. A SIMPLIFIED REMODELING OF THE BUILDING (FEW HOURS)
  58. 58. JUST THE EDITED MESH
  59. 59. THE DETAILED REMODELING OF THE BUILDING (SEVERAL DAYS)
  60. 60. THE DETAILED REMODELING OF THE BUILDING (SEVERAL DAYS)
  61. 61. A SIMPLIFIED REMODELING OF THE BUILDING (FEW HOURS)
  62. 62. REMODELING CHOICES • remodeling all at the maximum level of details would have required 1 week x about 7000 buildings: not feasible • a simplified approach could have been acceptable for the simpler structures, not for the monumental buildings, hoverer still time-consuming!
  63. 63. TWO CATEGORIES OF BUILDINGS WERE IDENTIFIED Urban fabric Monumental buildings
  64. 64. URBAN FABRIC HAS TO BE MODULAR… • The extension of the model and the relatively short time needed for sure an optimized assembly line • Monumental buildings developed singularly • Urban fabric developed with archetypes
  65. 65. OTHER CLUES • Gismondi left few documents • however some preparatory drawings have been found • they shows domus types studies
  66. 66. Approach #1: search of recurring elements and Maya modelling of a limited set of modules (library) –JANEZ DONNO, MASTER THESIS (2006)
  67. 67. RECURRING ELEMENTS IN ROOFS
  68. 68. ABACUS OF ROOFS
  69. 69. RECURRING ELEMENTS IN BUILDINGS • Pattern analysis on horizontal and vertical sections of the mesh • Classification of similarities
  70. 70. RESULTS • About 20 types of elementary building archetypes employed for 90% of the physical model • Used in the “Plastico” with variation of scale and in different combination hiding geometric repetitions
  71. 71. PRELIMINARY EXPERIMENT WITH MODULARIZED GEOMETRIES
  72. 72. Approach #2: search of recurring elements and procedural modeling of a class of buildings (object oriented) –IGNAZIO LUCENTI, MASTER THESIS (2007)
  73. 73. PROCEDURAL MODELING • Sofware used: Side Effects Houdini • General purpose procedural modeling package • Every item is considered as a flow of data and can be manipulated through a network of operators • Users can make their own custom operators and custom “prototypes” (called digital assets)
  74. 74. HOUDINI SCREENSHOT
  75. 75. BENEFITS OF THIS APPROACH • Models are made up of reusable parametric modules (e.g. a column asset can be used in every object that contains a column) • Updating the model became very easy because only the asset needs to be modified and all the instances are updated accordingly • For example, to update the temple models, add a texture or a new parameter, user needs to modify the prototype only and the changes will be reflected in every existing temple • It is possible to have different versions of the model, switching them automatically (e.g. different levels of detail based on camera distance) • Object parameters can be controlled manually, by algorithms, by data sources (database) or even by image maps
  76. 76. WORKFLOW • Definition of the object parameters (analysis) • Making a parametric model of the object • Turning it into a Digital Asset (prototype) with its own custom interface • Placing instances of the prototype on the 3D model of the “plastico” (manually or driven by a rule)
  77. 77. TEMPLES CLASSIFICATION Tholos Prostyle Peristyle Pseudo Pseudo sine postico Sine Postico
  78. 78. PARAMERIC ELEMENTS Column • Diameter • Height • Base height Roof • Entablature height • Roof slope • Frame taper Podium • Typology • Size (x,y,z) • Steps width • Step height
  79. 79. PROCEDURAL MODEL OF THE TEMPLES
  80. 80. BUILDING MODELED PROCEDURALLY • Temples • Bridges • Exterior city walls
  81. 81. DTM FROM THE PLASTICO MESH • All the buildings in the 3D scan have been deleted • All the consequent holes filled • The resulting mesh have been sliced in order to separate river, land, and paved roads (for assigning them different shaders)
  82. 82. ME RGE IN S INGL E DIGI TAL MODE L Included: • DTM (original mesh) • vernacular buildings (library) • Temples (procedural) • Bridges (procedural) • Walls (procedural)
  83. 83. Such 3D model, once integrated by Bernard Frischer’s group with the various high-detail models of monumental buildings (Forum, Coliseum, Circus Maximum, etc.), became the model known as Rome Reborn 1.0 It was also the starting point of the following fully procedural versions of RR, based on CityEngine: http://romereborn.frischerconsulting.com
  84. 84. LESSON LEARNED • Laser Radar technology can solve the difficult task of acquiring large artifacts with small details • The acquired data is always valuable but sometimes it is visually not sufficient for virtual reality • In that case the right post-processing approach may change dramatically the time needed for completing the model
  85. 85. 2007-8! •! 3D survey of the Pompeii Forum (Scuola Normale di Pisa)! •! POLIMI coordination, 3D scanning and modeling, integration, rendering! •! FBK Trento contributing with photogrammetry! Large area: • 150 m maximum length • 80 m maximum width • 8 large structures included • 377 small finds spread all over the area N 150 m 80 m
  86. 86. • Level of detail ranging from the geographical scale to the object scale • For each scale the more suitable survey technology was adopted • The consequent resolution varied from 25 cm to 0.2 mm
  87. 87. LOW-RESOLUTION (GEOGRAPHICAL AREA FRAMING) Digital surface model (DSM) of about 1 square km around the forum: • existing aerial Images for geometry capture • 1:3500 photogram scale • geometric resolution: 25 cm Acquisition of single points for image registration • GPS • Standard topographic approach Texture mapping from above • Pictometry images • 15 cm texture resolution
  88. 88. MEDIUM-RESOLUTION (LARGE STRUCTURES) Leica HDS 3000 laser scanner for long-range acquisitions (3D framing of the forum) Resolution 5-20 mm Leica HDS 6000 for fast and massive acquisitions (3D acquisition of areas with many occlusions) Resolution 5-10 mm Close range photogrammetry digital reflex cameras with manual processing Resolution: dynamically changing
  89. 89. HIGH-RESOLUTION (DETAILS) Close range photogrammetry with digital reflex cameras and automatic matching (ETH multi-photo matcher) Resolution: up to 0.5 mm
  90. 90. CATALOGUING THE RUINS • Each geometrical entity was identified, catalogued, photographed and coded • All the following work has been referred to such IDs for image storing and models management
  91. 91. LASER SCANNING • 10 days of scanning in two stages • 1.2 G points acquired • 100 M points used for modeling (1:10 ratio) • Heavy hand cleaning for deleting artifacts (visitors, spurious data) • ICP alignment • Sorting and subdivision with two outputs: • General reference for the whole model • Single sets of data for each structure
  92. 92. DIGITAL PHOTOGRAMETRY • 3200 images acquired with precalibrated cameras • Photogrammetric models metrically generated in their own reference • Aligned with the 3D scanned reference in the final integration stage
  93. 93. FINAL INTEGRATION
  94. 94. INTEGRATION PROCESS
  95. 95. 2011! •!3D survey and modeling of Temples in My Son (Vietnam)! •!Virtual reconstruction with strong integration between actual 3D data and other sources! 3D scanning with Faro Focus 3D Image based 3D and textures
  96. 96. FLYING OVER THE SITE
  97. 97. SCIENTIFIC RECONSTRUCTION
  98. 98. 3D RECONSTRUCTION P I P E L I N E
  99. 99. 2012! •! 3D survey and modeling of Certosa di Pavia (Italy)! •! Virtual reconstruction of several historical phases based on integration between real 3D and historical sources!
  100. 100. 2012-15! •! EU project 3D-ICONS: 3000+ models for EUROPEANA! •! POLIMI: massive digitization of 527 items! •!Wide use of automatic photogrammetry based on SFM!
  101. 101. 531 MODELS COMPLETED
  102. 102. 2007-! •! Metrologic analysis of 3D devices and methods! !"#$%&'$('#%)*+,-# ./#%)&(0# 1/#%)&(0# 2(3$4,(%)(*# 5&*)4$&6#7)+&3$,4# 89)4&*,4#0:$66# ;(<)4*&$(*=# !"#$%&'()*+,-.,)*/0'1'2$%*30*45$1'(1*$(&*67&"%'(18*.*5"2#7%71'9$%*$::#7$9;<)*='5"*>75:#"??'7(* ="9;(7%71'"?*6$1$@'(")*33-3A*BCDDEF*
  103. 103. ACCURACY, PRECISION & UNCERTAINTY !"#$%&'' ())%)''!' ./-0(&"0,+' *)(+,-,%#' 1))%)''!' 2++3)"+/' • In “modern” metrology uncertainty incorporates both concepts • Useful in standards for acceptance tests • Not for separately analyzing systematic and random error components
  104. 104. TEST OBJECTS FOR 3D NRC, Canada NPL, UK POLIMI, small volumes !"##$%%$ !"##$%%$ 64 mm 1 mm !"#$%# !&#$%# POLIMI, large volumes
  105. 105. !"#$%&'()$**$ +',-.$!/,00./$+!122$ #34(5&,$63$
  106. 106. METROLOGY FOR CH MODELING Laser scanned reference Ref. vs. Photogrammetric model - Agisoft Photoscan SW - Nikon D90 - Mean error = 1.18 mm - Std. dev. = 3.38 mm Ref. vs. Photogrammetric model - Autodesk Recap Web service - Nikon D600 - Mean error = 0.34 mm - Std. dev. = 1.80 mm Gabriele GUIDI, Bernard FRISCHER, Photomodeling vs. traditional 3D data capture of cultural heritage artifacts, Conference on Cultural Heritage and New Technologies, November 3-5 2014, Vienna, Austria
  107. 107. CONCLUDING REMARKS • In order to solve complex problems you have to go deeply into them. Many of the CH models shown could not be feasible if electronic engineering, informatics, archaeology, statistics, architecture, geomatics, computer graphics and metrology would not have interacted positively. The keyword is interdisciplinarity intended as action giving a result larger than the sum of the single disciplinar contributions • The 3D model is important but often it is not enough. In many case it is just a (fundamental) starting point for a documentation activity that necessarily involves an enrichment of such models, both geometric (3D semantics), visual (computer graphics) and informative (metadata & ontologies) • Similarly the 3D model can be used for communication purposes where the main issues are related with both local and remote 3D visualization (including virtual reality and augmented reality)
  108. 108. CONCLUDING REMARKS (2) • The technologies seen show that many of those models required months to be created. Although any experimentation is important it is clear that the future of 3D documentation can’t be that. It has to be quick! Only in this way it will be possible to handle problems of massive 3D digitization. Image based modeling integrated with laser scanning and smart 3D post processing techniques seems nowadays the most promising way • The quality of what your 3D data indicates what you can do with them. The traceability of the whole 3D acquisition pipeline (sensor, process, 3D model) is fundamental for a scientific use of 3D • The same concept can be extended to any 3D modeling activity in CH, including reconstructive modeling of something not anymore existing (philological traceability), obtained through a wise use of metadata documenting the process and the sources for generating the 3D model
  109. 109. CREDITS • Carlo Atzeni (Emeritus, retired from University of Florence, Italy) • Jean-Angelo Beraldin (National Research Council, Ottawa, Canada) • Bernard Frischer (University of Indiana, Bloomington, USA) • Fabio Remondino (FBK, Trento, Italy) • Alessandro Spinetti (Florence Engineering, Florence, Italy) • Tommaso Grasso (3dHPM, Rome, Italy) • Sara Lazzari (formerly Optonet, Brescia, Italy) • Grazia Tucci (University of Florence, Italy) • Monica De Simone (Director of Museo Archeologico di Rieti, Italy) • Claudia Angelelli (Università degli Studi di Padova,Italy) • Salvatore Barba (University of Salerno, Italy) • Carlo Bianchini (University of Rome “La Sapienza”, Italy) • Maurizio Seracini (UC, San Diego, USA) • Federico Uccelli (Leica Geosystems, Lodi, Italy) • Achim Lupus (Leica GEOSYSTEMS AG, Switzerland) • Patrizia Zolese (Fondazione Lerici, Rome, Italy) • Mara Landoni (Politecnico di Milano, Italy) • Sebastiano Ercoli (Politecnico di Milano, Italy)
  110. 110. thanks for your attention gabriele.guidi@polimi.it

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