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A Model Classification for Digital 3D Reconstruction in the Context of Humanities Research

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3D Research Challenges in Cultural Heritage II

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10025))

Abstract

Digital 3D reconstruction methods have been widely applied to support research and the presentation of historical objects since the 1990s. While technological backgrounds, project opportunities as well as methodological considerations for application are widely discussed in literature, a comprehensive, model classification for digital 3D reconstruction is still lacking. Against this background, this article aims to discuss common approaches to classification of scholarly work. The identification of specific issues and challenges in the context of humanities research is also discussed. A prototype classification scheme for digital reconstruction in humanities research is proposed. It has been applied to and tested in two case studies.

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Notes

  1. 1.

    http://www.merriam-webster.com/dictionary/classification. Accessed 31 May 2015.

  2. 2.

    http://www.merriam-webster.com/dictionary/categorize. Accessed 21 Nov 2015.

  3. 3.

    Relevant approaches and principles have been defined by Information Science, see [10].

  4. 4.

    Object-relation stands in contrast to process-related human interpretation which is required, e.g. for algorithm development in context of data-driven acquisition.

  5. 5.

    A distinction between Digital Humanities and eHumanities is complex. A usual definition determines e(nhanced)Humanities as cooperation between information technologies and Humanities to investigate research questions in the Humanities (c.f. http://www.bmbf.de/foerderungen/21126.php. Accessed 10 Jun 2015) while Digital Humanities means a hybrid discipline, combining computing and humanities methods and approaches [28].

  6. 6.

    On difficulties concerning the classification and transdisciplinary digital heritage as an ‘Agora’, which are also relevant to Cultural Heritage, see [29].

  7. 7.

    On the history of the discipline, see [41–44].

  8. 8.

    On the art term, see [45].

  9. 9.

    On the critique of style, see [46, pp. 20–32]. On the term style, see [47].

  10. 10.

    On the methods of iconography and iconology, see [46, pp. 33–49].

  11. 11.

    Cited according to [50, pp. 131].

  12. 12.

    For example: http://3dvisa.cch.kcl.ac.uk/project12.html. Accessed 26 May 2016.

  13. 13.

    On the concept of simulation see [63, pp. 33–41 and 68–69].

  14. 14.

    One can observe a new trend in the humanities called the “cognitive revolution”, which means adapting scientific methods to systematization or formalization of certain phenomena, see [65].

  15. 15.

    http://www.getty.edu/research/tools/vocabularies/aat/. Accessed 26 May 2016.

  16. 16.

    http://www.iconclass.org/. Accessed 26 May 2016.

  17. 17.

    Further discussed–although dealing with another subject-matter–in [82].

  18. 18.

    Further discussed in the chapter of Prechtel et al. which is also part of this book.

  19. 19.

    For example, removing alterations of statues introduced in the course of an earlier conservation treatment. Discussed in [94]; For the restoration of fragmented objects, see [95].

  20. 20.

    The approaches followed until now concentrated mainly on analyzing architectural plans. Discussed in [97, 98].

  21. 21.

    See [99].

  22. 22.

    For example: Creating simulations of ancient ventilation systems. See [101].

  23. 23.

    A definition of “simulation”: [63].

  24. 24.

    The distinction between the type of source and inherent knowledge relies on outer and inner source criticism as formal vs. content-based quality [105].

  25. 25.

    These prerequisites also apply to an automated model creation process, see [108].

  26. 26.

    Further discussed in the chapter of Kuroczynski et al. which is also part of this book.

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Acknowledgements

The authors wish to acknowledge the support of Dr. rer. nat. Nikolas Prechtel of the Technische Universität Dresden who provided information on the Ethno-Nature Park “Uch Enmek” project in order to test the proposed classification schema. Furthermore, the authors would like to thank the reviewer of the chapter for providing some suggestions: According to his suggestions, for example, a geometric fidelity could be characterized and distinguished as “simple geometric model”, “geometric model with meta-description-elements”, “enhanced model”, with relationships to further source material or/and time layers, or to express the degree of resolution expressed by numbers.

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  1. Media Center, Technische Universität Dresden, 01062, Dresden, Germany

    Sander Münster, Wolfgang Hegel & Cindy Kröber

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  1. Sander Münster
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  2. Wolfgang Hegel
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  3. Cindy Kröber
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Correspondence to Sander Münster .

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Editors and Affiliations

  1. TU Dresden , Dresden, Germany

    Sander Münster

  2. TU Darmstadt , Darmstadt, Germany

    Mieke Pfarr-Harfst

  3. Herder Institute for Historical Research on East Central Europe, Marburg, Germany

    Piotr Kuroczyński

  4. Cyprus University of Technology , Limassol, Cyprus

    Marinos Ioannides

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Münster, S., Hegel, W., Kröber, C. (2016). A Model Classification for Digital 3D Reconstruction in the Context of Humanities Research. In: Münster, S., Pfarr-Harfst, M., Kuroczyński, P., Ioannides, M. (eds) 3D Research Challenges in Cultural Heritage II. Lecture Notes in Computer Science(), vol 10025. Springer, Cham. https://doi.org/10.1007/978-3-319-47647-6_1

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