跨模式深度学习框架用于江木雕遗产的3D重建和信息整合
1Keyi College, Zhejiang Sci-Tech University, Shaoxing, 312369, China.
Scientific reports
|December 5, 2025
概括
本研究介绍了一种适应性深度学习框架,用于木雕的3D重建. 它通过动态加权数据源来提高准确性,优于传统文物保护的现有方法.
科学领域:
- 数字遗产是数字遗产的一部分.
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 传统的3D重建方法使用固定重量融合,忽略遗产文物中的局部表面细节.
- 由于复杂的表面特征,江木雕具有独特的挑战.
研究的目的:
- 开发一种适应性的跨模式深度学习框架,用于精确地3D重建木雕遗产.
- 加强文献和保护文化遗产文物.
主要方法:
- 实施了面积复杂性意识的门网,用于动态模式权重 (几何和视觉).
- 采用混合方法,将激光扫描和RGB-D数组结合起来,用于数据采集.
- 在300个注释的江木雕艺术品的数据集上验证了框架.
主要成果:
- 实现了0.52毫米的孔距离和86.7%的F-Score,分别超过了3D高斯裂纹的20%和6.8%.
- 在语义细分中达到76.3%的欧盟交叉点 (mIoU) 平均值,比仅点云方法有11.7%的改进.
- 在保存复杂的开工和浮雕图案方面表现出卓越的性能.
结论:
- 适应性融合策略有效地处理异质木雕表面,设定了新的准确性基准.
- 该框架为记录遗产收藏提供了前所未有的精度,有助于虚拟学徒和遗产管理.
- 实现自动编目和定量恶化评估,用于智能遗产资源管理.
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