Abstract
A great deal of time, patience, and effort are required to excavate pottery. For example, archaeologists dig hundreds to thousands of pottery shards from an excavation site. However, restoring pottery is a time-consuming and challenging process, requiring considerable amounts of expertise, experience, and time. Therefore, computer-assisted restoration methods are indispensable to assist the pottery restoration process. However, existing restoration approaches mostly resort to heuristic-based approaches, which are computationally expensive to match and align different shards together. It is often infeasible to handle and process a large number of shards to reconstruct pottery in 3D. In this paper, we propose a deep learning-based pottery restoration algorithm to classify a pottery shard to a specific pottery type and further predict the exact shard location in the pottery type. We use a novel 3D Convolutional Neural Networks and Skip-dense layers to achieve these objectives. Our model first processes a 3D point cloud data of each shard and predicts the shape of the pottery, which a shard possibly belongs to. We first apply Dynamic Graph CNN to effectively perform learning on 3D point clouds of shards and use Skip-dense layers for a classifier. In particular, we generate features from the 3D scanned point cloud of each shard using spatial transform and edge convolution, then classify shards into one of the pottery shape types using Skip-dense. We achieve 98.4% of classification accuracy over 5 different pottery types and 0.032 RMSE for shard location prediction.
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Acknowledgement
This research is supported by the Ministry of Science and ICT (MSIT), Korea, under the ICT Consilience Creative Program (IITP-2020-2011-1-00783) supervised by the Institute for Information and communication Technology Planning and evaluation (IITP), and the Ministry of Culture, Sports, and Tourism (MCST) and the Korea Creative Content Agency (KOCCA) in the Culture Technology (CT) Research and Development Program 2020. Also, this research was supported by Energy Cloud R&D Program through the National Research Foundation (NRF) of Korea Funded by the Ministry of Science, ICT (No. 2019M3F2A1072217) and was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2017R1C1B5076474 and No. 2020R1C1C1006004).
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Kim, K., Hong, J., Rhee, SH., Woo, S.S. (2021). Reconstructing the Past: Applying Deep Learning to Reconstruct Pottery from Thousands Shards. In: Dong, Y., Ifrim, G., Mladenić, D., Saunders, C., Van Hoecke, S. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track. ECML PKDD 2020. Lecture Notes in Computer Science(), vol 12461. Springer, Cham. https://doi.org/10.1007/978-3-030-67670-4_3
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DOI: https://doi.org/10.1007/978-3-030-67670-4_3
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