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© Fraunhofer IGD Sebastian Pena Serna Enriching 3D Collections Fraunhofer-Institut für Graphische Datenverarbeitung IGD Fraunhoferstraße 5 64283 Darmstadt Tel +49 6151 155 – 468 [email protected] www.igd.fraunhofer.de

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Page 1: S. Pena Serna - Enriching

© Fraunhofer IGD

Sebastian Pena Serna

Enriching 3D Collections

Fraunhofer-Institut für Graphische Datenverarbeitung IGD Fraunhoferstraße 5 64283 Darmstadt Tel +49 6151 155 – 468 [email protected] www.igd.fraunhofer.de

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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

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Workflow with 3D collections

Accessing: search and browse

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Workflow with 3D collections

Accessing: search and browse

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Building a 3D collection

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Multimedia Information

Collections management Conservation

Bibliographic Images

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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

2IvoryPanel_DocEvent.rdf

forms_part_of

forms_part_of

A.15-1955-dome-out.rdf

A.15-1955-dome-out.zip

has_created

2009CA5306_0.rdf

2009CR4851_0.rdf

2009CA5306_0.tif

2009CR4851_0.tif

has_created

4Ivory_Arc3DProcEvent.rdf

used_as_derivation_source

Arc3D-A.15-1955_dmy.v3d

created_derivative

5Ivory_MeshLabProcEvent.rdf

used_as_derivation_source 2009CA5307v Coloured.ply

created_derivative

digitized

3IvoPan_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

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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 Annotation

Approach 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 Annotation

Strategies: 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 Annotation

Strategies: 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 Annotation

Issues:

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

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Workflow with 3D collections

Accessing: search and browse

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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

Cloud Computing

3D Internet

Mobile devices

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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

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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.

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Thank You!

Sebastian Pena Serna Fraunhofer-Institut für Graphische Datenverarbeitung IGD Fraunhoferstraße 5 64283 Darmstadt Tel +49 6151 155 – 468 [email protected] www.igd.fraunhofer.de

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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

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