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San Vitale Challenge: Automatic Reconstruction of Ancient Colored Glass Windows
Di Domenico, Nicolò ✉
;
Borghi, Guido
;
Franco, Annalisa
;
Boschetti, Marco
;
Giacomini, Federica
;
Barzaghi, Sebastian
;
Ferucci, Silvia
;
Zambruno, Simone
;
Mularoni, Lorenzo
;
Gao, Qiong
;
Che, Chenyue
;
Li, Guoxin
;
Zu, Yanyan
;
Hao, Jiayao
;
Zhang, Junpei
;
Dúcz, Ákos
;
Gegő, Levente
;
Imeri, Klevis
;
Nemkin, Viktória [Nemkin, Viktória (algoritmuselmélet...), szerző] Számítástudományi és Információelméleti Tanszék (BME / VIK)
;
Rakhmatillaev, Azam
;
Szatmári, Soma
;
Rowan, William
Angol nyelvű Szakcikk (Folyóiratcikk) Tudományos
Megjelent:
LECTURE NOTES IN COMPUTER SCIENCE 0302-9743 1611-3349
LNCS
(15628)
pp. 263-278
2025
Konferencia:
18th European Conference on Computer Vision, ECCV 2024 2024-09-29 [Milan, Olaszország]
X. Földtudományok Osztálya: A
SJR Scopus - Computer Science (miscellaneous): Q2
Azonosítók
MTMT: 36181166
DOI:
10.1007/978-3-031-91572-7_16
WoS:
001544981100016
Scopus:
105006893447
Egyéb URL:
https://link.springer.com/chapter/10.1007/978-3-031-91572-7_16
Szakterületek:
Számítógépes látás
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. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
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2026-08-15 11:43
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