TAD-Net: tooth axis detection network based on rotation transformation encoding

IF 2.5 4区计算机科学 Q2 COMPUTER SCIENCE, SOFTWARE ENGINEERING Graphical Models Pub Date : 2022-05-01 DOI:10.1016/j.gmod.2022.101138

Yeying Fan , Qian Ma , Guangshun Wei , Zhiming Cui , Yuanfeng Zhou , Wenping Wang

引用次数: 0

Abstract

The tooth axes, defined on 3D tooth model, play a key role in digital orthodontics, which is usually used as an important reference in automatic tooth arrangement and anomaly detection. In this paper, we propose an automatic deep learning network (TAD-Net) of tooth axis detection based on rotation transformation encoding. By utilizing quaternion transformation, we convert the geometric rotation transformation of the tooth axes into the feature encoding of the point cloud of 3D tooth models. Furthermore, the feature confidence-aware attention mechanism is adopted to generate dynamic weights for the features of each point to improve the network learning accuracy. Experimental results show that the proposed method has achieved higher detection accuracy on the constructed dental data set compared with the existing networks.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

TAD-Net:基于旋转变换编码的齿轴检测网络

在三维牙齿模型上定义的牙轴在数字正畸中起着关键作用，通常作为自动排牙和异常检测的重要参考。本文提出了一种基于旋转变换编码的齿轴检测自动深度学习网络(TAD-Net)。利用四元数变换，将齿轴的几何旋转变换转化为三维齿模型点云的特征编码。进一步，采用特征置信度感知注意机制，对每个点的特征生成动态权值，提高网络的学习精度。实验结果表明，与现有网络相比，该方法在构建的牙齿数据集上取得了更高的检测精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Graphical Models 工程技术-计算机：软件工程

CiteScore

3.60

自引率

5.90%

发文量

审稿时长

47 days

期刊介绍： Graphical Models is recognized internationally as a highly rated, top tier journal and is focused on the creation, geometric processing, animation, and visualization of graphical models and on their applications in engineering, science, culture, and entertainment. GMOD provides its readers with thoroughly reviewed and carefully selected papers that disseminate exciting innovations, that teach rigorous theoretical foundations, that propose robust and efficient solutions, or that describe ambitious systems or applications in a variety of topics. We invite papers in five categories: research (contributions of novel theoretical or practical approaches or solutions), survey (opinionated views of the state-of-the-art and challenges in a specific topic), system (the architecture and implementation details of an innovative architecture for a complete system that supports model/animation design, acquisition, analysis, visualization?), application (description of a novel application of know techniques and evaluation of its impact), or lecture (an elegant and inspiring perspective on previously published results that clarifies them and teaches them in a new way). GMOD offers its authors an accelerated review, feedback from experts in the field, immediate online publication of accepted papers, no restriction on color and length (when justified by the content) in the online version, and a broad promotion of published papers. A prestigious group of editors selected from among the premier international researchers in their fields oversees the review process.