Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Cross-Modal Search and Exploration of Greek Painted Pottery

Elisabeth Trinkl, Stephan Karl, Stefan Lengauer, Reinhold Preiner, Tobias Schreck

TL;DR

The paper addresses the need for digitally enhanced, cross-modal exploration of Greek painted pottery by integrating 3D shape, material, and painting data. It presents a suite of methods, including 3D data acquisition (laser, photogrammetry, CT), advanced unwrapping via Elastic Flattening, volume estimation, and both shape- and motif-based retrieval, demonstrated through multiple case studies. Key contributions include a physics-inspired approach to distortion-minimized surface unwrapping, CT-enabled interior analysis for manufacturing techniques, and linked-views for multivariate structuring of large vase collections, enabling non-destructive cross-location comparisons. The work highlights practical significance for archaeology by improving documentation, searchability, and visualization, while outlining future directions in data standardization, scalable digitization, and machine learning for cross-domain retrieval.

Abstract

This paper focuses on digitally-supported research methods for an important group of cultural heritage objects, the Greek pottery, especially with figured decoration. The design, development and application of new digital methods for searching, comparing, and visually exploring these vases needs an interdisciplinary approach to effectively analyse the various features of the vases, like shape, decoration, and manufacturing techniques, and relationships between the vases. We motivate the need and opportunities by a multimodal representation of the objects, including 3D shape, material, and painting. We then illustrate a range of innovative methods for these representations, including quantified surface and capacity comparison, material analysis, image flattening from 3D objects, retrieval and comparison of shapes and paintings, and multidimensional data visualization. We also discuss challenges and future work in this area.

Cross-Modal Search and Exploration of Greek Painted Pottery

TL;DR

The paper addresses the need for digitally enhanced, cross-modal exploration of Greek painted pottery by integrating 3D shape, material, and painting data. It presents a suite of methods, including 3D data acquisition (laser, photogrammetry, CT), advanced unwrapping via Elastic Flattening, volume estimation, and both shape- and motif-based retrieval, demonstrated through multiple case studies. Key contributions include a physics-inspired approach to distortion-minimized surface unwrapping, CT-enabled interior analysis for manufacturing techniques, and linked-views for multivariate structuring of large vase collections, enabling non-destructive cross-location comparisons. The work highlights practical significance for archaeology by improving documentation, searchability, and visualization, while outlining future directions in data standardization, scalable digitization, and machine learning for cross-domain retrieval.

Abstract

This paper focuses on digitally-supported research methods for an important group of cultural heritage objects, the Greek pottery, especially with figured decoration. The design, development and application of new digital methods for searching, comparing, and visually exploring these vases needs an interdisciplinary approach to effectively analyse the various features of the vases, like shape, decoration, and manufacturing techniques, and relationships between the vases. We motivate the need and opportunities by a multimodal representation of the objects, including 3D shape, material, and painting. We then illustrate a range of innovative methods for these representations, including quantified surface and capacity comparison, material analysis, image flattening from 3D objects, retrieval and comparison of shapes and paintings, and multidimensional data visualization. We also discuss challenges and future work in this area.
Paper Structure (14 sections, 10 figures)

This paper contains 14 sections, 10 figures.

Table of Contents

  1. Introduction
  2. Methods of 3D data acquisition of small-scale objects -- an overview
  3. Applications in pottery research -- case studies
  4. Unwrappings of painted curved surfaces
  5. Shape comparison
  6. Filling volume calculation
  7. Identification of manufacturing techniques
  8. Shape-based retrieval
  9. Motif-based retrieval
  10. Multivariate structuring of large object collections
  11. Discussion and Outlook
  12. Challenges
  13. Outlook
  14. Conclusion

Figures (10)

  • Figure 1: Computer-aided rollouts of the Corinthian alabastron University Graz G 28: (\ref{['sfig:1a']}) photo; (\ref{['sfig:1b']}) cylindrical; (\ref{['sfig:1c']}) conical rollout. © S. Karl, J. Kraschitzer, University of Graz
  • Figure 2: Attic red-figure hydria, University Graz G 30; (\ref{['sfig:2a']}) photo; (\ref{['sfig:2b']}) spherical rollout exhibiting proportional (yellow) and angular distortions (white); (\ref{['sfig:2c']}) Elastic Flattening. © Preiner et al. 2018, The Eurographics Association
  • Figure 3: (\ref{['sfig:3a']}) Elastic flattening of the Corinthian alabastron University Graz G 28 in comparison to (\ref{['sfig:3b']}) a hand-drawn unwrapping of the alabastron Brussels R 224 with comparable motiv from the same vase painter lenormant1858. © R. Preiner, TU Graz
  • Figure 4: Three interdependent series of head vases stored in nine different collections. © P. Bayer, E. Trinkl, University of Graz
  • Figure 5: Corinthian alabastron, University Graz G 28: (\ref{['sfig:5a']}) Isosurface volume rendering of ct data (transparent modus); (\ref{['sfig:5b']}) ct surface with incisions, one half enhanced by using Multi Scale Integral Invariant filtering; (\ref{['sfig:5c']}) textured ct surface with sectioning; (\ref{['sfig:5d']}) volumetric "phantom" body of the capacity (1493 ml). © S. Karl, University of Graz
  • ...and 5 more figures