通过基于移动设备的多视角立体摄影监测正畸治疗中的牙齿运动。

Jiaming Xie, Congyi Zhang, Guangshun Wei, Peng Wang, Guodong Wei, Wenxi Liu, Min Gu, Ping Luo, Wenping Wang
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引用次数: 0

摘要

如今,牙齿矫正已成为现代个人生活的重要组成部分,可帮助人们改善咀嚼功能,提高自尊心。然而,正畸治疗的质量在很大程度上仍依赖于经验丰富的医生的经验评价,缺乏量化评估,患者需要经常到诊所进行当面检查。为了解决上述问题,我们提出了一种新颖实用的基于移动设备的框架,用于精确测量治疗过程中的牙齿移动情况,从而简化和强化传统的牙齿监测过程。为此,我们将牙齿移动监测任务表述为一个多视角多物体姿态估计问题,通过不同视角捕捉多个无纹理和严重闭塞的物体(即牙齿)。具体来说,我们利用预先扫描的三维牙齿模型和稀疏的多视角牙齿图像集作为我们提出的牙齿监测框架的输入。从初始配置中提取牙齿轮廓并定位每个视图的初始相机姿态后,我们提出了一种联合姿态估计方案,以精确估计每颗牙齿的三维姿态,从而推断出它们在治疗过程中的相对偏移。此外,我们还引入了 "相对姿势偏差 "指标,以评估小范围内单个牙齿姿势的准确性。我们证明了我们的方法能够达到实际正畸治疗监控所需的高精度和高效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tooth Motion Monitoring in Orthodontic Treatment by Mobile Device-based Multi-view Stereo.

Nowadays, orthodontics has become an important part of modern personal life to assist one in improving mastication and raising self-esteem. However, the quality of orthodontic treatment still heavily relies on the empirical evaluation of experienced doctors, which lacks quantitative assessment and requires patients to visit clinics frequently for in-person examination. To resolve the aforementioned problem, we propose a novel and practical mobile device-based framework for precisely measuring tooth movement in treatment, so as to simplify and strengthen the traditional tooth monitoring process. To this end, we formulate the tooth movement monitoring task as a multi-view multi-object pose estimation problem via different views that capture multiple texture-less and severely occluded objects (i.e. teeth). Specifically, we exploit a pre-scanned 3D tooth model and a sparse set of multi-view tooth images as inputs for our proposed tooth monitoring framework. After extracting tooth contours and localizing the initial camera pose of each view from the initial configuration, we propose a joint pose estimation scheme to precisely estimate the 3D pose of each individual tooth, so as to infer their relative offsets during treatment. Furthermore, we introduce the metric of Relative Pose Bias to evaluate the individual tooth pose accuracy in a small scale. We demonstrate that our approach is capable of reaching high accuracy and efficiency as practical orthodontic treatment monitoring requires.

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