# Implant Model Generation Method for Mandibular Defect Based on Improved 3D Unet
## 1 Introduction
The main methods can be classified as follows: (1) The classic mesh processing method: the 3D model is expressed as a 3D triangular mesh and can be directly processed; [PERSON] et al. [15] present an effective method for filling holes in 3D triangular meshes based on the direct solution of Laplace and biharmonic partial differential equations. The proposed approach is accurate and effective in filling triangular mesh holes of different sizes and shapes. (2) The processing method based on 3D point cloud data, which uses the symmetry of the 3D point cloud for shape completion; and (3) methods based on deep learning. Deep learning methods play an important role in 3D shape completion owing to various publicly available object 3D shape datasets, such as ShapeNet, which convert various objects (such as cars, chairs, airplanes, etc.) into voxel form and use a network of encoder-decoder structures for training [16]. [PERSON] et al. [17] propose a variational autoencoder with graph convolution operations that can learn occluded or missing parts in fully realistic shapes. The restoration of mandibular defects is a typical 3D shape completion problem, which is to restore the shape and structure of the bone defect part and design the implant model. However, these above-mentioned methods are not suitable for mandibular shape completion. These methods deal with simple geometries that have the same or a similar shape and structure, whereas the morphological structure of the mandible is relatively complex and varies from person to person. In recent years, deep learning has been widely used in the medical field [18; 19]. [PERSON] et al. [20] first proposed a deep learning method in 2019 to perform 3D shape completion on the defective skull model. This method was verified on an entire reconstructed skull without a validation of the defect implant. The deep learning approach adopted by [PERSON] et al. [21] uses two neural networks step by step. First, the network is trained to reconstruct a low-dimensional 3D model to locate the defect area. Then, another neural network is trained to achieve high-dimensional implant predictions. [PERSON] et al. [22] proposed a convolutional neural network with an autoencoder structure with auxiliary paths to form 3D implants by inpainting 2D slices with different axes. [PERSON] et al. [23] proposed a reconstruction-subtraction strategy for skull repair, which consists of two steps: first, the complete skull is reconstructed, and then the implant model is generated by subtracting the defect model from the complete skull compared to the direct reconstruction method using implants; the 3D model of the implant constructed by the reconstruction-subtraction strategy produces more noise. [PERSON] et al. [24] used an improved 3D Unet for skull repair research. However, the shape and structure of the mandible differ greatly from those of the skull, the size and shape of the mandible vary greatly from person to person, and the location and shape of the defect are also complex and variable. The above-mentioned deep learning methods cannot effectively extract complex mandibular and defect morphological information and cannot complete accurate mandibular defect repair. [PERSON] et al. [25] incorporated dilated convolution into the encoder-decoder structure to expand the receptive field and enhance the ability of information extraction. However, the increase in the network depth will lead to network degradation and gradient disappearance, which limits the effect of defect repair. In addition, the datasets used in these studies were constructed using the simulated defect method [26], but the skull defect shapes are some simple geometric shapes, such as cylinders, which limits the generalization of the network. In addition, since there is no public 3D shape dataset about defective mandibles, the application of deep learning methods in mandible shape completion is constrained. At present, there is no relevant literature on the application of deep learning methods in mandibular defect repair.