从解耦角度看可微分碰撞监督齿排列网络

Zhihui He, Chengyuan Wang, Shidong Yang, Li Chen, Yanheng Zhou, Shuo Wang
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引用次数: 0

摘要

牙齿排列是数字正畸规划过程中必不可少的一步。现有的基于学习的方法使用隐藏的牙齿特征直接回归牙齿运动,将目标姿势感知和运动回归结合在一起。这可能导致对三维变换的感知不佳。它们还忽略了预测牙列中牙齿之间可能存在的重叠或间隙,这通常是不可接受的。因此,我们提出了一种可区分的碰撞监督牙齿排列网络--DTAN,它将预测任务和特征建模分离开来。DTAN 将牙齿排列任务解耦,首先预测最终牙齿排列的隐藏特征,然后利用这些特征辅助回归起始牙齿和目标牙齿之间的运动。为了更好地学习隐藏特征,DTAN 还将牙齿隐藏特征分解为几何特征和位置特征,并通过特征一致性约束对其进行进一步监督。此外,我们还为点云数据提出了一种新颖的可微分碰撞损失函数,用于约束牙齿之间的相关手势,该函数可轻松扩展到其他三维点云任务中。我们提出了一种名为 C-DTAN 的牙弓宽度引导的牙齿排列网络,以实现结果的可控性。我们构建了三个不同的牙齿排列数据集,与现有方法相比,在准确性和速度上都有大幅提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Differentiable Collision-Supervised Tooth Arrangement Network with a Decoupling Perspective
Tooth arrangement is an essential step in the digital orthodontic planning process. Existing learning-based methods use hidden teeth features to directly regress teeth motions, which couples target pose perception and motion regression. It could lead to poor perceptions of three-dimensional transformation. They also ignore the possible overlaps or gaps between teeth of predicted dentition, which is generally unacceptable. Therefore, we propose DTAN, a differentiable collision-supervised tooth arrangement network, decoupling predicting tasks and feature modeling. DTAN decouples the tooth arrangement task by first predicting the hidden features of the final teeth poses and then using them to assist in regressing the motions between the beginning and target teeth. To learn the hidden features better, DTAN also decouples the teeth-hidden features into geometric and positional features, which are further supervised by feature consistency constraints. Furthermore, we propose a novel differentiable collision loss function for point cloud data to constrain the related gestures between teeth, which can be easily extended to other 3D point cloud tasks. We propose an arch-width guided tooth arrangement network, named C-DTAN, to make the results controllable. We construct three different tooth arrangement datasets and achieve drastically improved performance on accuracy and speed compared with existing methods.
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