基于牙中心注意力的3D牙模语义分割新方法，提升复杂排列牙齿的分割精度。
TCATSeg: A Tooth Center-Wise Attention Network for 3D Dental Model Semantic Segmentation
- 以牙中心为引导构建稀疏超点，融合全局语义上下文信息
- 在400个牙模数据集上达到当前最优分割性能，显著优于现有方法
- 适用于正畸与种植牙等数字牙科场景，尤其适合相邻牙齿相似情况
精确的3D牙模语义分割对正畸和种植牙等数字牙科应用至关重要。然而，由于牙齿排列复杂且邻近牙齿形状相似，现有方法因过度关注局部几何特征而忽略全局上下文信息，导致分割不准确。为此，我们提出TCATSeg，一种结合局部几何特征与全局语义上下文的新框架。通过引入一组稀疏但具有物理意义的超点，有效捕捉全局语义关系，提升分割精度。此外，我们构建了一个包含400个牙模的数据集，涵盖正畸前样本，用于评估方法泛化能力。大量实验表明，TCATSeg在多种指标上均优于现有最先进方法。
原文摘要 · Abstract (English)
Accurate semantic segmentation of 3D dental models is essential for digital dentistry applications such as orthodontics and dental implants. However, due to complex tooth arrangements and similarities in shape among adjacent teeth, existing methods struggle with accurate segmentation, because they often focus on local geometry while neglecting global contextual information. To address this, we propose TCATSeg, a novel framework that combines local geometric features with global semantic context. We introduce a set of sparse yet physically meaningful superpoints to capture global semantic relationships and enhance segmentation accuracy. Additionally, we present a new dataset of 400 dental models, including pre-orthodontic samples, to evaluate the generalization of our method. Extensive experiments demonstrate that TCATSeg outperforms state-of-the-art approaches.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。