用AI一键生成精准牙冠模型，省去手动调整烦恼。
From Mesh Completion to AI Designed Crown
- 输入牙齿点云，通过变换器完成牙冠区域的智能补全
- 平均切比雪夫距离达0.062，精度优于传统变形方法
- 适合牙科数字化设计人员，提升修复体建模效率
牙冠设计耗时且依赖人工调整。本文提出端到端的牙科网格补全（DMC）方法，基于包含目标牙及其邻牙和对颌牙的点云上下文，生成完整牙冠网格。首先通过特征提取器将点云转换为局部特征向量，再经变压器预测缺失区域的特征，随后由点重建头与多层感知机生成稠密带法向的点集，最后通过可微点到网格层重建表面网格。在自建数据集上的实验表明，该方法平均切比雪夫距离为0.062，显著优于基于图卷积神经网络的网格变形方法。代码已开源。
原文摘要 · Abstract (English)
Designing a dental crown is a time-consuming and labor intensive process. Our goal is to simplify crown design and minimize the tediousness of making manual adjustments while still ensuring the highest level of accuracy and consistency. To this end, we present a new end- to-end deep learning approach, coined Dental Mesh Completion (DMC), to generate a crown mesh conditioned on a point cloud context. The dental context includes the tooth prepared to receive a crown and its surroundings, namely the two adjacent teeth and the three closest teeth in the opposing jaw. We formulate crown generation in terms of completing this point cloud context. A feature extractor first converts the input point cloud into a set of feature vectors that represent local regions in the point cloud. The set of feature vectors is then fed into a transformer to predict a new set of feature vectors for the missing region (crown). Subsequently, a point reconstruction head, followed by a multi-layer perceptron, is used to predict a dense set of points with normals. Finally, a differentiable point-to-mesh layer serves to reconstruct the crown surface mesh. We compare our DMC method to a graph-based convolutional neural network which learns to deform a crown mesh from a generic crown shape to the target geometry. Extensive experiments on our dataset demonstrate the effectiveness of our method, which attains an average of 0.062 Chamfer Distance.The code is available at:https://github.com/Golriz-code/DMC.gi
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