SHREC 2021: Retrieval of cultural heritage objects

• Published in: Computers & graphics Vol. 100; pp. 1 - 20
• Main Authors: Sipiran, Ivan, Lazo, Patrick, Lopez, Cristian, Jimenez, Milagritos, Bagewadi, Nihar, Bustos, Benjamin, Dao, Hieu, Gangisetty, Shankar, Hanik, Martin, Ho-Thi, Ngoc-Phuong, Holenderski, Mike, Jarnikov, Dmitri, Labrada, Arniel, Lengauer, Stefan, Licandro, Roxane, Nguyen, Dinh-Huan, Nguyen-Ho, Thang-Long, Perez Rey, Luis A., Pham, Bang-Dang, Pham, Minh-Khoi, Preiner, Reinhold, Schreck, Tobias, Trinh, Quoc-Huy, Tonnaer, Loek, von Tycowicz, Christoph, Vu-Le, The-Anh
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2021; Elsevier Science Ltd
• Subjects: 3D model retrieval; Benchmarking; Cultural heritage; Cultural resources; Culture; Datasets; Evaluation; Object recognition; Retrieval; Shape recognition; 3D model retrieval; Benchmarking; Cultural heritage
• ISSN: 0097-8493; 1873-7684
• DOI: 10.1016/j.cag.2021.07.010

•We present a benchmark of cultural heritage objects for the evaluation of 3D shape retrieval methods.•We propose two semantically different challenges: retrieval by shape and retrieval by culture.•Ten teams around the world presented techniques to address the challenges, mainly using learning approaches.•Our experiments and results show that learning methods on image-based multi-view representation are suitable for tackling 3D retrieval in a cultural heritage domain. [Display omitted] 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.