SPMMSegNet: self-parameterization multi-scale mesh segmentation network for tooth identification

Hezi Shi

doi:10.1007/s00371-026-04556-1

2026-06-20

PRE

Hezi Shi

Jianmin Zheng *

DOI：10.1007/s00371-026-04556-1

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摘要

En 中文

自动全口牙齿分割与识别从口内扫描（IOS）网格中是一项数字正畸学和修复过程中的基础任务。然而，这存在结构上的困难：牙齿边界很薄，需要精确的局部推理，而牙齿身份则取决于全局排列和沿牙弓的长程几何上下文。现有的基于网格的方法缺乏几何对齐的跨分辨率对应关系。在本文中，我们提出了自参数化多尺度网格分割网络（SPMMSegNet），这是一种端到端的网格原生架构，通过尺度结构化传播来应对这一挑战。特征聚合被限制在精细分辨率下的内在表面拓扑结构中，以保留边界细节，而欧几里得聚合被引入最粗尺度以捕捉牙齿内和牙弓级上下文。使用双射表面对应关系实现了几何一致的跨尺度特征传输。特别设计的牙弓感知位置编码和顺序一致性正则化进一步将解剖结构整合到学习中。通过在一个统一的网格框架内结合表面拓扑、全局上下文和解剖先验，SPMMSegNet为全口牙齿识别提供了有效的解决方案。

Keyword:

3D mesh segmentation

Dental mesh analysis

Graph attention networks

Multi-scale mesh learning

期刊

The Visual Computer

IF:

论文数:

369

被引数:

机构

College of Computing and Data Science

学者数:

论文数: 40

被引数: 0

引用论文

DilatedToothSegNet: Tooth Segmentation Network on 3D Dental Meshes Through Increasing Receptive Vision DilatedToothSegNet：一种通过增大感受野对3D牙科网格进行牙齿分割的网络

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