## 1 Introduction
For the hole-filling task, the hole regions can be filled with the same parts of a complete 3D model, which is generated by a trained network. Therefore, the core task of deep learning-based methods is to build and train a network to generate a realistic 3D model. Depending on the type of 3D models, these generation-oriented methods can be further categorized into two classes: volume-based methods and point cloud-based methods. The volume-based method converts the input point cloud into a regular 3D voxel grid, over which the 3D convolutional neural networks (3D-CNNs) can be employed to learn the latent information in the voxel model without hole regions. Finally, the trained network is used to generate a complete voxel model [23; 27; 28]. However, the main limitation of volume-based methods is the quantization loss of the geometric information due to the low resolution enforced by voxelization. Since the original point cloud can represent 3D models more concisely, the point cloud-based methods, which can process the point cloud directly and obtain a better result compared to the volume-based methods, has become a hot topic. The first deep learning architecture for directly processing point cloud is PointNet t [29], and many other PointNet-based methods have also been proposed in recent years [30; 31; 32]. These methods can learn the latent features by using an encoder-decoder network, which can be used to generate point cloud data with similar shapes [33; 34; 35]. [PERSON] et al. [36] combined an auto-encoder with a generative adversarial network (GAN) to generate point cloud by using the latent feature learned from the auto-encoder. To reduce the time consumption of the network, [PERSON] et al. [37] introduced a real-time RL-GAN network to control GAN, which generated the latent representation of the shape that best fits the current input of incomplete point cloud. However, the point cloud-based methods required the size of the point cloud to be a constant value. For example, the input size was usually 1024 or 2048. Due to the complex and varied structures of 3D CHarts, it is impossible to input them into the point cloud-based network directly unless we down-sample the point cloud. Unfortunately, a large number of details will be eliminated through the downsampling process. In this paper, a novel framework, named MU-GAN, is proposed to fill these hole regions of 3D CHarts. Firstly, a detecting hole method is proposed. Secondly, a point cloud patch with hole regions is extracted as the input of the trained MU-GAN to generate a dense and complete point cloud. Finally, the generated point cloud is used to fill the hole regions of the original point cloud. Experimental results demonstrate that our framework can achieve a complete point cloud with high quality. The main contributions of this work can be summarized as follows:  
1. A multi-scale upsampling GAN (MU-GAN) is built to generate a dense point cloud to fill the hole regions. 2. To make the learned latent feature more robust, multi-scale point clouds are used to complete the incomplete models, which can restore the CHarts with more fine-grained details. 3. There are no limitations on the size of the input point cloud, indicating that the proposed framework is more suitable for the CHarts with a huge point number to preserve details.