Post on 23-Dec-2014
description
© Fraunhofer IGD
Sebastian Pena Serna
Enriching 3D Collections
Fraunhofer-Institut für Graphische Datenverarbeitung IGDFraunhoferstraße 564283 Darmstadt
Tel +49 6151 155 – 468sebastian.pena.serna@igd.fraunhofer.de www.igd.fraunhofer.de
© Fraunhofer IGD 2
3D Collection
Digital archive with multimedia material and 3D artifacts, which is associated with semantic information
Building
Acquisition and ingestion of digital assets and their corresponding provenance information
Accessing
Browsing and exploration of digital assets in the 3D collection
Enriching
Increasing the associations within the semantic network
Definitions
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Workflow with 3D collections
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Workflow with 3D collections
Building:
acquire
and
process
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Workflow with 3D collections
Accessing:search and
browse
Building:
acquire
and
process
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Workflow with 3D collections
Enriching:
view and
annotate
Accessing:search and
browse
Building:
acquire
and
process
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Building a 3D collection
Building:
acquire
and
process
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Multimedia Information
Collections managementCollections management ConservationConservation
BibliographicBibliographicImagesImages
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3D geometry
Material properties
Digital provenance
Digitization
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Improve the quality of 3D artifacts
Process 3D artifacts for different purposes (e.g. research, presentation)
Processing
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Legacy and rich processing metadata
Provenance
1IvoryPanel_ObjAcqEvent.rdf
1IvoryPanel_ObjAcqEvent.rdf
2IvoryPanel_DocEvent.rdf
2IvoryPanel_DocEvent.rdf
forms_part_of
forms_part_of
A.15-1955-dome-out.rdfA.15-1955-dome-out.rdf
A.15-1955-dome-out.zip
has_created
2009CA5306_0.rdf2009CA5306_0.rdf
2009CR4851_0.rdf2009CR4851_0.rdf
…
2009CA5306_0.tif
…
2009CR4851_0.tif
has_created
4Ivory_Arc3DProcEvent.rdf4Ivory_Arc3DProcEvent.rdf
used_as_derivation_source
Arc3D-A.15-1955_dmy.v3d
created_derivative
5Ivory_MeshLabProcEvent.rdf
5Ivory_MeshLabProcEvent.rdf
used_as_derivation_source 2009CA5307v
Coloured.ply
created_derivativedigitized
3IvoPan_LegacyData.rdf3IvoPan_LegacyData.rdf
Digitization_Process
Formal_Derivation
Sub-events
Data_Object
Legend
Man_Made_Object
forms_part_of
has_created
forms_part_of
IvoryPanel
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Individual objects with high-quality metadata
Ingestion
Large acquisition campaigns with similar structures
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Accessing a 3D collection
Accessing:search and
browse
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Stanford Repository
3D artifacts without searchable metadata
Metadata Accessing
http://www-graphics.stanford.edu/data/3Dscanrep/
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AIM@SHAPE
3D artifacts with basic searchable metadata, e.g. categories, keywords
Metadata Accessing
http://shapes.aim-at-shape.net/
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3D-COFORM
3D artifacts with rich metadata
Fundamental categories and relationships
Searchable material and shape properties
Metadata Accessing
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Administrator
User Accessing
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CH professional
User Accessing
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Internet user
User Accessing
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Enriching a 3D collection
Enriching:
view and
annotate
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3D Shape Annotation
Aim: associate digital 3D shapes with related information and knowledge on the represented object
Annotation: mechanism for enriching digital 3D shapes with semantics
Result: annotated shape or a semantically enriched shape, combining:
the geometric description
contextual information
knowledge of the represented object
the created relationships
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Sponsors
Projects:
AIM@SHAPE (http://www.aimatshape.net/)
Focus K3D (http://www.focusk3d.eu/)
3D-COFORM (www.3d-coform.eu)
V-MusT (http://www.v-must.net/)
Enhancing Engagement with 3D Heritage Data through Semantic Annotation (http://www.ddsgsa.net/projects/empire/Empire/Home.html)
Semantic Annotations for 3D Artefacts (http://itee.uq.edu.au/~eresearch/projects/3dsa)
Technologies:
Linking Open Data (http://esw.w3.org/SweoIG/TaskForces/CommunityProjects/LinkingOpenData)
3D Internet (Alpcan et al. 2007 [33])
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Annotation Process
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Annotation Process
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Geometric Definition
Aim:
Understand the intrinsic structure of the digital 3D shape (Attene et al. 2006 [1], De Floriani et al. 2010 [2])
Associate semantics with relevant part(s) of the digital 3D shape (Spagnuolo and Felcidieno 2009 [3])
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Geometric Definition
Techniques:
Sketching, painting, outlining, fitting, segmenting, and structuring
These are driven by different principles (Attene at al. 2006 [4], Shamir 2008 [5] and Chen et al. 2009 [6])
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Principles:
RANSAC (Schnabel et al. 2007 [7])
Curvature analysis (Madeira et al. 2007 [8])
Contour analysis (Liu and Zhang 2007 [9])
Discrete operators (Reuter et al. 2009 [10])
Physics (Fang et al. 2011 [11])
Concavity (Au et al. 2011 [12])
Geometric Definition
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Geometric Definition
Strategies:
Hierarchical segmentation (Shapira et al. 2010 [13], Wang et al. 2011 [14], Ho and Chuang 2011 [15])
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Geometric Definition
Strategies:
Combination of geometric principles with other concepts about the represented shape (Attene et al. 2009 [16], Golovinsliy and Fankhouser 2009 [17], Kalogerakis et al. 2010 [18]).
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Strategies:
Skeletons to identify the structure of the digital 3D (Tierny et al. 2007 [19], Shapira et al. 2008 [20]) and/or by means of fitting primitives (Attene et al. 2006 [21]).
Geometric Definition
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Geometric Definition
Strategies:
User assisted segmentation for complex digital 3D shapes or for additional requirements, e.g. functions or styles (De Floriani et al. 2008 [22], Miao et al. 2009 [23], Bergamasco et al. 2011 [24]).
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Geometric Definition
Strategies:
Manual segmentation, sketching (Ji et al. 2006 [25]), painting (Papaleo and De Floriani 2010 [26]) or outlining regions (Pena Serna et al. 2011 [27]).
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Geometric Definition
Strategies:
Segmentation refinement (Klaplansky and Tal 2009 [28]).
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Geometric Definition
Specific Requirements:
Scenes (Knopp et al. 2011 [29])
Developable segments (Julius et al. 2005 [30])
Best view (Mortara and Spagnuolo 2009 [31]).
Identify adjectives (Simari et al. 2009 [32])
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Geometric Definition
Challenges:
Difficult to generate a plausible and context-aware geometric definition for different classes of objects.
The current strategies cannot easily be mapped to the different applications’ requirements within a given domain.
There are few approaches trying to map principles to specific applications’ requirements.
A combination of principles, strategies and user guidance could generate the expected results.
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Annotation Process
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Structured Information and Knowledge
There is a vast amount of existent information and knowledge related to any digital 3D shape:
Information related to the intrinsic structure of the 3D shape
Information related to the meaning of the represented object
Information related to the digital provenance
Knowledge related to the application domain
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Structured Information and Knowledge
Structured Information for describing the intrinsic structure of the digital 3D shape (Papaleo and De Floriani 2010 [26], Attene et al. 2009 [16]).
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Structured Information and Knowledge
Structured Information for describing digital 3D shapes using concepts within a particular domain (Catalano et al. 2009 [34], De Luca et al. 2011 [35], Mortara et al. 2006 [36]).
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(Spatial Corp.)
Structured Information and Knowledge
Structured Information in the engineering domain
Product and Manufacturing Information (PMI)
Geometric Dimensions and Tolerances (GD&T)
Functional Tolerancing and Annotation (FT&A).
Standard ASME Y14.41-2003 Digital Product Data Definition Practices
ISO 1101:2004 Geometrical Product Specifications (GPS) - Geometrical tolerancing.
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Structured Information and Knowledge
Structured Information in the Cultural Heritage domain based on CIDOC-CRM http://cidoc.ics.forth.gr/ (Rodriguez-Echavarria et al. 2009 [37], Havemann et al. 2009 [38]).
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Annotation Process
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Mechanisms for Annotating
Different mechanisms have been proposed, which vary depending on:
application domain
degree of user intervention that they require
technology supporting them
degree of structured information which they involve.
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Mechanisms for Annotating
Application domain
Product design (Andre and Sorito 2002 [39])
Architecture (Pittarello and Gatto 2011 [40])
Cultural Heritage (Hunter and Gerber 2010 [41])
Chemistry (Gawronski and Dumontier 2011 [42])
Medicine (Trzupek et al. 2011 [43])
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Mechanisms for Annotating
User intervention
Semi-automatic mechanisms normally require of a degree of user intervention to define an annotation (Shapira et al. 2010 [13], Kalogerakis et al. 2010 [18]).
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Siemens NX
Mechanisms for Annotating
Supporting technology:
stand-alone modeling systems
stand-alone 3D viewers (Pena Serna et al. 2011 [27])
web based viewers (Hunter et al. 2010 [44])
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Annotation Process
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Representation of the AnnotationApproach to structure, store and transmit the
annotating process output
Important for the annotation’s indexing, retrieval and reutilization.
There is no agreed format for this.
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Representation of the AnnotationStrategies: Persistent annotations
Store the annotation in a database based on a semantic model.
The model describes the associations or relations between different media ([16], [27], Hunter et al. 2010 [45]).
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Representation of the AnnotationStrategies: Transient annotations
Store and transmit annotations in a data file.
MPEG-7 (Bilasco et al. 2006 [46])
VRML / X3D (Pittarello and Faveri 2006 [47], [40], [26])
Jupiter (JT) Data Format
Product Representation Compact (PRC) Data Format
COLLADA ([37], [38])
Universal 3D Data Format
ASME Y14.41 Digital Product Definition Data Practices
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Representation of the AnnotationIssues:
Stability, flexibility and easy of use
There is no notion of annotation representation.
It is considered as a piece of text, which is stored in a database or as a tag on a digital 3D shape.
Annotations’ interoperability
Degree of independency from transient digital 3D shapes.
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Enriching a 3D collection
Challenges and Opportunities
This remains an active area of research. Different challenges need to be solved to fully support a semantic enrichment pipeline:
Automatically extracting information from a digital 3D shape
Modeling semantic information
Automatically linking it to the digital 3D shape
Using standards to store, interoperate, and preserve annotations in the long term
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Enriching a 3D collection
Challenges and Opportunities
Opportunities of using semantically aware 3D shapes:
searching 3D shapes
intelligently interacting with semantically aware 3D shapes
shape matching or deriving meaning of new shapes
high-level editing
goal oriented 3D synthesizing
knowledge management
semantic visualization and interaction
© Fraunhofer IGD 54
Workflow with 3D collections
Enriching:
view and
annotate
Accessing:search and
browse
Building:
acquire
and
process
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Cloud Computing
Storage and computation capacity online
3D Internet
Visualization of 3D artifacts on standard web browsers
Mobile devices
Access and visualization on the move
Enabling Technologies
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Define workflows
Create services
Enable intuitive access
Provide contextualized interfaces
User involvement and engagement
Emerging Challenges
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References
[1] ATTENE M., BIASOTTI S., MORTARA M., PATANÉ G., SPAGNUOLO M., FALCIDIENO B.: Computational methods for understanding 3D shapes. Computers & Graphics 30, 3 (June 2006), 323–333.
[2] DE FLORIANI L., MAGILLO P., PAPALEO L., PUPPO E.: Shape modeling and understanding: Research trends and results of the G3 group at DISI.
[3] SPAGNUOLO M., FALCIDIENO B.: 3D media and the semantic web. IEEE Intelligent Systems (March/April 2009), 90–96.
[4] ATTENE M., KATZ S., MORTARA M., PATANÉ G., SPAGNUOLO M., TAL A.: Mesh segmentation - a comparative study. In Shape Modeling International (2006).
[5] SHAMIR A.: A survey on mesh segmentation techniques. Computer Graphics Forum 27, 6 (2008), 1539–1556.
[6] CHEN X., GOLOVINSKIY A., FUNKHOUSER T.: A benchmark for 3D mesh segmentation. In ACM SIGGRAPH 2009 papers (New Orleans, Louisiana, 2009), ACM, pp. 1–12.
[7] SCHNABEL R., WAHL R., KLEIN R.: Efficient RANSAC for Point-Cloud shape detection. Computer Graphics forum 26, Number 2 (June 2007), 214–226.
[8] MADEIRA J., SILVA S., STORK A., PENA SERNA S.: Principal Curvature-Driven segmentation of mesh models: A preliminary assessment. In 15 EPCG - Encontro Português de Computação Gráfica. (2007).
[9] LIU R., ZHANG H.: Mesh segmentation via spectral embedding and contour analysis. Volume 26 (2007), Number 3.
[10] REUTER M., BIASOTTI S., GIORGI D., PATANÉ G., SPAGNUOLO M.: Discrete Laplace-Beltrami operators for shape analysis and segmentation. Computers & Graphics 33, 3 (June 2009), 381–390.
[11] FANG Y., SUN M., KIM M.: Heat-Mapping: a robust approach toward perceptually consistent mesh segmentation. IEEE Computer Vision and Pattern Recognition (CVPR) 2011 (2011), pp 2145–2152.
[12] AU O. K., ZHENG Y., CHEN M., XU P., TAI C.: Mesh segmentation with concavity-aware fields. IEEE Trans. Vis. Comp. Graphics (2011).
[13] SHAPIRA L., SHALOM S., SHAMIR A., COHEN-OR D., ZHANG H.: Contextual part analogies in 3D objects. Int. J. Comput. Vision 89, 2-3 (2010), 309–326.
[14] WANG Y., XU K., LI J., ZHANG H., SHAMIR A., LIU L., CHENG Z., XIONG Y.: Symmetry hierarchy of Man-Made objects. Computer Graphics Forum 30, 2 (2011), 287–296.
[15] HO T., CHUANG J.: Volume based mesh segmentation. Journal of Information Science and Engineering 27 (2011).
[16] ATTENE M., ROBBIANO F., SPAGNUOLO M., FALCIDIENO B.: Characterization of 3D shape parts for semantic annotation. Computer-Aided Design 41, 10 (Oct. 2009), 756–763.
[17] GOLOVINSKIY A., FUNKHOUSER T.: Consistent segmentation of 3D models. Computers & Graphics 33, 3 (June 2009), 262–269.
[18] KALOGERAKIS E., HERTZMANN A., SINGH K.: Learning 3D Mesh Segmentation and Labeling. ACM Transactions on Graphics 29, 3 (2010).
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[19] TIERNY J., VANDEBORRE J.-P., DAOUDI M.: Topology driven 3d mesh hierarchical segmentation. In Proceedings of the IEEE International Conference on Shape Modeling and Applications 2007 (Washington, DC, USA, 2007), IEEE Computer Society, pp. 215–220.
[20] SHAPIRA L., SHAMIR A., COHEN-OR D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. The Visual Computer: International Journal of Computer Graphics 24, 4 (Mar. 2008).
[21] ATTENE M., FALCIDIENO B., SPAGNUOLO M.: Hierarchical mesh segmentation based on fitting primitives. The Visual Computer: International Journal of Computer Graphics 22 (2006), 181–193.
[22] DE FLORIANI L., PAPALEO L., CARISSIMI N.: A Java3D framework for inspecting and segmenting 3D models. In Proceedings of the 13th international symposium on 3D web technology (Los Angeles, California, 2008), ACM, pp. 67–74.
[23] MIAO Y., FENG J., WANG J., JIN X.: User-controllable mesh segmentation using shape harmonic signature. Progress in Natural Science 19, 4 (Apr. 2009), 471–478.
[24] BERGAMASCO F., ALBARELLI A., TORSELLO A.: Semi-supervised segmentation of 3D surfaces using a weighted graph representation. In Proceedings of the 8th international conference on Graph-based representations in pattern recognition (GbRPR’11) (2011).
[25] JI Z., LIU L., CHEN Z., WANG G.: Easy mesh cutting. Computer Graphics Forum 25, 3 (2006), 283–291.
[26] PAPALEO L., DE FLORIANI L.: Manual segmentation and semantic-based hierarchical tagging of 3D models. (2010) pp. 25–32.
[27] PENA SERNA S., SCOPIGNO R., DOERR M., THEODORIDOU M., GEORGIS C., PONCHIO F., STORK A.: 3D-centered media linking and semantic enrichment through integrated searching, browsing, viewing and annotating. In VAST11: The 12th International Symposium on Virtual Reality, Archaeology and Intelligent Cultural Heritage (Prato, Italy, 2011).
[28] KAPLANSKY L., TAL A.: Mesh segmentation refinement. In Computer Graphics Forum (Pacific Graphics), 28(7) (Oct. 2009), pp. 1995–2003.
[29] KNOPP J., PRASAD M. , VAN GOOL L. : Scene Cut: Class-specific Object Detection and Segmentation in 3D Scenes. In 3DIMPVT, Hangzhou, 2011
[30] JULIUS D., KRAEVOY V., SHEFFER A.: D-charts: Quasi-developable mesh segmentation. In Computer Graphics Forum, Proceedings of Eurographics 2005 (Dublin, Ireland, 2005), vol. 24, Eurographics, Blackwell, pp. 581–590.
[31] MORTARA M., SPAGNUOLO M.: Semantics-driven best view of 3D shapes. Computers & Graphics 33, 3 (June 2009), 280–290.
[32] SIMARI P., NOWROUZEZAHRAI D., KALOGERAKIS E., SINGH K.: Multi-objective shape segmentation and labeling. In Proceedings of the Symposium on Geometry Processing (Berlin, Germany, 2009), Eurographics Association, pp. 1415–1425.
[33] ALPCAN T., BAUCKHAGE C., KOTSOVINOS E.: Towards 3d internet: Why, what, and how? In Proceedings of the International Conference on Cyberworlds CW ’07 (October 2007), pp. 95 – 99.
[34] CATALANO C., CAMOSSI E., FERRANDES R., CHEUTET V., SEVILMIS N.: A product design ontology for enhancing shape processing in design workflows. Journal of Intelligent Manufacturing 20, 5 (Oct. 2009), 553–567. 3
References
© Fraunhofer IGD
References
[35] LUCA L. D., BUSAYARAT C., STEFANI C., VÉRON P., FLORENZANO M.: A semantic-based platform for the digital analysis of architectural heritage. Computers & Graphics 35, 2 (Apr. 2011), 227–241.
[36] MORTARA M., PATANÉ G., SPAGNUOLO M.: From geometric to semantic human body models. Computers&Graphics 30, 2 (Apr. 2006), 185–196.
[37] RODRIGUEZ ECHAVARRIA K., MORRIS D., ARNOLD D.: Web based presentation of semantically tagged 3D content for public sculptures and monuments in the UK. In Proceedings of the 14th International Conference on 3D Web Technology (Darmstadt, Germany, 2009), ACM, pp. 119–126.
[38] HAVEMANN S., SETTGAST V., BERNDT R., EIDE., FELLNER D. W.: The Arrigo showcase reloaded - towards a sustainable link between 3D and semantics. J. Comput. Cult. Herit. 2, 1 (2009), 1–13.
[39] ANDRE P., SORITO R.: Product manufacturing information (PMI) in 3D models: a basis for collaborative engineering in product creation process (PCP). In 14th European Simulation Symposium and Exhibition (2002).
[40] PITTARELLO F., GATTO I.: ToBoA-3D: an architecture for managing top-down and bottom-up annotated 3D objects and spaces on the web. In Web3D ’11 Proceedings of the 16th International Conference on 3D Web Technology (2011).
[41] HUNTER J., GERBER A.: Harvesting community annotations on 3D models of museum artefacts to enhance knowledge, discovery and re-use. Journal of Cultural Heritage 11, 1 (2010), 81–90.
[42] GAWRONSKI A., DUMONTIER M.: MoSuMo: a semantic web service to generate electrostatic potentials across solvent excluded protein surfaces and binding pockets. Computers & Graphics 35, 4 (Aug. 2011), 823–830.
[43] TRZUPEK M., OGIELA M. R., TADEUSIEWICZ R.: Intelligent image content semantic description for cardiac 3D visualisations. Engineering Applications of Artificial Intelligence In Press, Corrected Proof (2011).
[44] HUNTER J., YU C.-H., NAKATSU R., TOSA N., NAGHDY F., WONG K., CODOGNET P.: Supporting multiple perspectives on 3D museum artefacts through interoperable annotations. Vol. 333 of IFIP Advances in Information and Communication Technology. Springer Boston, 2010, pp. 149–159.
[45] HUNTER J., COLE T., SANDERSON R., VAN DE SOMPEL H.: The open annotation collaboration: A data model to support sharing and interoperability of scholarly annotations. (2010)
[46] BILASCO I. M., GENSEL J., VILLANOVA-OLIVER M., MARTIN H.: An MPEG-7 framework enhancing the reuse of 3D models. In Proceedings of the eleventh international conference on 3D web technology (Columbia, Maryland, 2006), ACM, pp. 65–74.
[47] PITTARELLO F., FAVERI A. D.: Semantic description of 3D environments: a proposal based on web standards. In Proceedings of the eleventh international conference on 3D web technology (Columbia, Maryland, 2006), ACM, pp. 85–95.
© Fraunhofer IGD 60
Thank You!
Sebastian Pena SernaFraunhofer-Institut für Graphische Datenverarbeitung IGDFraunhoferstraße 564283 Darmstadt
Tel +49 6151 155 – 468sebastian.pena.serna@igd.fraunhofer.de www.igd.fraunhofer.de
© Fraunhofer IGD 61
Access and enrichment of 3D collections
Searching and browsing
Searching: flexible formulation of queries
Browsing: exploration of multiple results and query refinement
Viewing and Annotating
Viewing: inspection and analysis of multimedia objects
Annotating: building and enrichment of semantic relationships
IVB: Integrated Viewer / Browser
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IVB: Searching and Browsing Interface
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IVB: Viewing and Annotating Interface