TUCNet:一个基于道和空间注意力的图形卷积网络,用于牙取样和完成
Mengting Liu1, Xiaojie Li2, Jie Liu1
1School of Computer and Information Technology, Beijing Jiaotong University, 100044, Beijing, China.
Computers in biology and medicine
|October 6, 2023
概括
通过智能填充缺少的数据,TUCNet有效地完成了稀缺的3D牙科模型. 这种新的方法提高了3D牙点云的质量,以改善牙科应用.
科学领域:
- 计算机视觉 计算机视觉
- 医疗成像医学成像
- 牙科技术 牙科技术
背景情况:
- 3D扫描在牙科中越来越多地用于口腔捕获.
- 来自3D扫描的稀少和不完整的点云数据对牙科应用提出了挑战.
- 高品质的3D牙科模型对于假牙和正牙至关重要.
研究的目的:
- 开发一种高分辨率的牙点云完成方法 (TUCNet).
- 为解决稀疏口头点云数据中缺少信息的问题.
- 从不完整的扫描生成密集和完整的3D牙点云.
主要方法:
- 拟议的通道和空间注意边缘Conv (CSAE) 模块用于融合本地和全球点特征.
- 开发了一个基于CSAE的点云升级样本 (CPCU) 模块,用于逐步加密点云.
- 采用基于树的方法,用于层次点云生成跳过连接.
主要成果:
- 在一个专门的牙点云完成数据集上,TUCNet实现了最先进的性能.
- 该方法在一般PCN数据集上表现出色.
- 实验结果验证了TUCNet在生成完整点云方面的有效性.
结论:
- TUCNet成功完成了稀疏的3D牙科模型,产生了高分辨率和密集的点云.
- 拟议的CSAE和CPCU模块对于特征提取和点云上采样是有效的.
- 这种方法为牙科健康应用中的3D数据处理提供了重大进步.
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