Reconstructing, Understanding, and Analyzing Relief Type Cultural Heritage from a Single Old Photo
Authors: 
Jiao Pan, Liang Li, Hiroshi Yamaguchi, Kyoko Hasegawa, Fadjar Ibnu Thufail, Brahmantara, Xiaojuan Ban, Satoshi TanakaAuthors Info & Claims
Jiao Pan, Liang Li, Hiroshi Yamaguchi, Kyoko Hasegawa, Fadjar Ibnu Thufail, Brahmantara, Xiaojuan Ban, Satoshi TanakaAuthors Info & ClaimsPages 7724 - 7733
Abstract
Relief-type cultural heritage objects are commonly found at historical sites but often manifest with varying degrees of damage and deterioration. The traditional process of reconstructing these reliefs is laborious and requires extensive manual intervention and specialized archaeological knowledge. By utilizing a single old photo containing predamage information of a given relief, monocular depth estimation can be used to reconstruct 3D digital models. However, extracting depth variations along the edges is challenging in relief scenario due to the highly compression of the depth values, resulting in low-curvature edges. This paper proposes an innovative solution that leverages a multi-task neural network to enhance the depth estimation task by integrating the edge detection and semantic segmentation tasks. We redefine edge detection of relief data as a multi-class classification task rather than a typical binary classification task. In this paper, an edge matching module that performs this novel task is proposed to refine depth estimations specifically for edge regions. The proposed approach achieves better depth estimation results with finer details along the edge region. Additionally, the semantic and edge outputs provide a comprehensive reference for multi-modal understanding and analysis.
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Published In
October 2024
11719 pages
ISBN:9798400706868
DOI:10.1145/3664647
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- Dong Xu
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Published: 28 October 2024
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MM '24
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MM '24: The 32nd ACM International Conference on Multimedia
October 28 - November 1, 2024
Melbourne VIC, Australia
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MM '24 Paper Acceptance Rate 1,150 of 4,385 submissions, 26%;
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
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