SHREC 2021: Retrieval of cultural heritage objects

Ivan Sipiran (Corresponding author), Patrick Lazo, Cristian Lopez, Milagritos Jimenez, Nihar Bagewadi, Benjamin Bustos, Hieu Dao, Shankar Gangisetty, Martin Hanik, Ngoc-Phuong Ho-Thi, Mike J. Holenderski, Dmitri S. Jarnikov, Arniel Labrada, Stefan Lengauer, Roxane Licandro, Dinh-Huan Nguyen, Thang-Long Nguyen-Ho, Luis A. Perez Rey, Bang-Dang Pham, Minh-Khoi PhamReinhold Preiner, Tobias Schreck, Quoc-Huy Trinh, Loek M.A. Tonnaer, Christoph von Tycowicz, The-Anh Vu-Le

Research output: Contribution to journalArticleAcademicpeer-review

11 Citations (Scopus)
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This paper presents the methods and results of the SHREC’21 track on a dataset of cultural heritage (CH) objects. We present a dataset of 938 scanned models that have varied geometry and artistic styles. For the competition, we propose two challenges: the retrieval-by-shape challenge and the retrieval-by-culture challenge. The former aims at evaluating the ability of retrieval methods to discriminate cultural heritage objects by overall shape. The latter focuses on assessing the effectiveness of retrieving objects from the same culture. Both challenges constitute a suitable scenario to evaluate modern shape retrieval methods in a CH domain. Ten groups participated in the challenges: thirty runs were submitted for the retrieval-by-shape task, and twenty-six runs were submitted for the retrieval-by-culture task. The results show a predominance of learning methods on image-based multi-view representations to characterize 3D objects. Nevertheless, the problem presented in our challenges is far from being solved. We also identify the potential paths for further improvements and give insights into the future directions of research.
Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalComputers and Graphics
Publication statusPublished - Nov 2021


  • benchmarking
  • 3D Model Retrieval
  • Cultural Heritage


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