@inproceedings{
pan2024reconstructing,
title={Reconstructing, Understanding, and Analyzing Relief Type Cultural Heritage from a Single Old Photo},
author={Jiao PAN and Liang Li and Hiroshi Yamaguchi and Kyoko Hasegawa and Fadjar Ibnu Thufail and Brahmantara and Xiaojuan Ban and Satoshi Tanaka},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=6Ekv9Egl3j}
}
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. This paper not only advances in computer vision task computer vision tasks but also provides effective technical support for the protection of relief-type cultural heritage objects.
Reconstructing, Understanding, and Analyzing Relief Type Cultural Heritage from a Single Old Photo
Jiao PAN Liang Li Hiroshi Yamaguchi Kyoko Hasegawa Fadjar Ibnu Thufail Brahmantara Xiaojuan Ban Satoshi Tanaka