While complete dental models are crucial for digital dentistry, current technologies mostly focus on the 3D dental crown but overlook the dental gum that is important for applications in orthodontics and prosthodontics. To reconstruct the complete dental models with visually realistic geometry from the given crown data, we propose to combine the implicit function representation with the self-attention mechanism. Recent studies have shown that the implicit function is an effective 3D representation for shape completion. However, existing methods fail in dealing with dental models with complex shapes and details, because the convolution and linear operations adopted in their networks are inefficient for modeling long-range dependencies or hard to maintain detailed geometry of the shapes. Therefore, we propose to introduce self-attention to the implicit function network for the first time and use it to effectively capture non-local features at different levels. Extensive ablation studies were conducted to validate the efficiency of our method. Quantitative and qualitative comparisons demonstrate that the feature extracted by our network is more expressive and thus leads to better dental model completion and reconstruction results.

虽然完整的牙科模型对于数字牙科至关重要，但当前的技术主要集中在 3D 牙冠上，而忽略了对正畸和修复学应用很重要的牙龈。为了从给定的牙冠数据重建具有视觉逼真几何形状的完整牙齿模型，我们建议将隐式函数表示与自注意力机制相结合。最近的研究表明，隐函数是形状完成的有效 3D 表示。然而，现有方法无法处理具有复杂形状和细节的牙科模型，因为其网络中采用的卷积和线性运算对于建模长距离依赖性或难以维护形状的详细几何形状效率低下。所以，我们建议首次将自注意力引入隐函数网络，并使用它来有效捕获不同级别的非局部特征。进行了广泛的消融研究以验证我们方法的效率。定量和定性比较表明，我们的网络提取的特征更具表现力，从而导致更好的牙科模型完成和重建结果。