基于照片引导的3D口腔扫描模型牙齿分割
Photo-Guided Tooth Segmentation on 3D Oral Scan Model
将口内照片的纹理语义作为补充信息，通过相机对齐和特征过滤门控融合到3D扫描模型的分割中，为多模态3D分割提供了可复用的配准与特征选择思路。
原文依据 we propose a novel Photo-guided 3D Model Tooth Segmentation framework, PMTSeg, that enhances 3D tooth segmentation by integrating texture cues from intraoral photos.
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论精确的3D牙齿分割是数字牙科、正畸分析和临床模拟的基础。口内扫描（IOS）模型通常存在纹理信息不完整或不可靠的问题，导致难以勾勒牙齿与牙龈之间的精细边界，而2D口内图像提供了丰富的语义和色彩信息，可以补充3D几何。因此，我们提出了一种新颖的照片引导3D模型牙齿分割框架PMTSeg，通过整合口内照片的纹理线索来增强3D牙齿分割。我们的框架引入了三个关键组件：用于精确图像-模型配准的相机对齐模块（CAM）、用于自适应多视图特征选择的特征过滤门控（FFG），以及用于学习纹理-几何对应关系的一致性特征学习（CFL）机制。我们的方法支持任意数量和视角的口内照片。实验表明，在区分相邻牙齿和牙齿-牙龈边界方面有显著改进，证明口内照片作为3D扫描的高效、语义丰富的补充，可用于精确的牙齿分割。
英文原文摘要 1162 字符
Accurate 3D tooth segmentation is fundamental for digital dentistry, orthodontic analysis, and clinical simulation. Intraoral scan (IOS) models often suffer from incomplete or unreliable texture information, making it difficult to delineate fine boundaries between teeth and gingiva, while 2D intraoral images provide rich semantic and chromatic information that can complement 3D geometry. Thus, we propose a novel Photo-guided 3D Model Tooth Segmentation framework, PMTSeg, that enhances 3D tooth segmentation by integrating texture cues from intraoral photos. Our framework introduces three key components: a Camera Alignment Module (CAM) for accurate image-model registration, a Feature Filtering Gate (FFG) for adaptive multi-view feature selection, and a Consistent Feature Learning (CFL) mechanism for learning texture-geometry correspondence. Our method supports arbitrary numbers and views of intraoral photos. Experiments show significant improvements in distinguishing adjacent teeth and tooth-gingiva boundaries, demonstrating that intraoral photographs serve as an efficient, semantically rich supplement to 3D scans for precise dental segmentation.