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The sixth-century Basilica of San Vitale in Ravenna, Italy, once featured intricate circular colored glass windows that illuminated its interior. Although these windows are now lost, several fragments were recovered during recent restorations. Unfortunately, reconstructing the original glass windows from these fragments is extremely complex and time-consuming, requiring the use of specialized expertise. Therefore, the development of automatic reconstruction techniques based on Artificial Intelligence is particularly important and challenging, due to, for instance, the presence of uniform color, damaged glass edges, and many fragment outliers. In this direction, the San Vitale Challenge was organized to gather the best methods and algorithms, as described and summarized in this paper. The challenge, split into several sub-tracks of increasing difficulty and realism, received the submission of several solutions, ranging from more classical computer vision algorithms to purely deep learning-based approaches, whose results are quantitatively evaluated and compared. In the last part of the paper, directions for future developments of such systems are discussed.

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San Vitale Challenge: Automatic Reconstruction of Ancient Colored Glass Windows

  • 会议论文

  • 发表日期：

    2025年

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  • Nicolò Di Domenico,
  • Guido Borghi,
  • Annalisa Franco,
  • Marco Boschetti,
  • Federica Giacomini,
  • Sebastian Barzaghi,
  • Silvia Ferucci,
  • Simone Zambruno,
  • Lorenzo Mularoni,
  • Qiong Gao,
  • Chenyue Che,
  • Guoxin Li,
  • Yanyan Zu,
  • Jiayao Hao,
  • Junpei Zhang,
  • Ákos Dúcz,
  • Levente Gegő,
  • Klevis Imeri,
  • Viktória Nemkin,
  • Azam Rakhmatillaev,
  • Soma Szatmári,
  • William Rowan

摘要

The sixth-century Basilica of San Vitale in Ravenna, Italy, once featured intricate circular colored glass windows that illuminated its interior. Although these windows are now lost, several fragments were recovered during recent restorations. Unfortunately, reconstructing the original glass windows from these fragments is extremely complex and time-consuming, requiring the use of specialized expertise. Therefore, the development of automatic reconstruction techniques based on Artificial Intelligence is particularly important and challenging, due to, for instance, the presence of uniform color, damaged glass edges, and many fragment outliers. In this direction, the San Vitale Challenge was organized to gather the best methods and algorithms, as described and summarized in this paper. The challenge, split into several sub-tracks of increasing difficulty and realism, received the submission of several solutions, ranging from more classical computer vision algorithms to purely deep learning-based approaches, whose results are quantitatively evaluated and compared. In the last part of the paper, directions for future developments of such systems are discussed.