## 1 Introduction  
High-pressure turbine blades are the core hot-end components of aeroengines. Detecting the coating surface of blades with complex shapes and cross-sections, and obtaining surface microstructure information, including stress distribution, are of great significance for guiding the preparation, subsequent processing, and service of turbine blades [1]. X-ray diffraction is one of the most widely used and most accurate characterization methods for studying the microstructure of materials. In particular, high-intensity synchrotron radiation X-rays can perform high-sensitive, high-spatial-resolution, and high-energy resolution analysis to obtain essential structural information such as the internal crystal structure, stress-strain, phase transition, and defects of the thermal barrier coating [2, 3].  
To determine the coordinates of the measured point and the X-ray incident angle, point cloud data are adopted to perform 3D modeling at the beamline BL14B1 [4] in the Shanghai Synchrotron Radiation Facility (SSRF), which is shown in Figure 1. Point cloud data of the blade's surface are collected with a 3D camera. In the 3D model, the XYZ coordinate information and the X-ray incidence angle are defined and the target point to be detected is selected. Then, the normal direction of that point is calculated. According to the mapping of the 3D model and the real samples, the blade mounted on the six-axis motorized platform is driven to the desired position. Finally, the diffraction data are collected to analyze the stress information.  
However, the point cloud data generated by the 3D camera is relatively sparse and has certain deviations, which cannot meet the accuracy requirements needed to measure the residual stress of the target point accurately. The upsampling algorithm can process the original data to generate dense, complete, and uniform point cloud data [5]. With the rapid development of the deep learning method for point clouds [6; 7; 8], point cloud upsampling research based on deep learning has achieved state-of-the-art results, such as PU-Net [9], EC-Net [10], MPU [11], PU-GCN [12], and PU-GAN [13]. However, the point cloud obtained from the 3D camera contains a lot of noisy data. Inputting the raw data directly into those models will further amplify the noises. Some subtle features will be lost, and some new noise points and non-uniform points will be generated. If it occurs in the region of interest (ROI) of the blade's surface, the wrong point may be selected for the subsequent experiment. This problem can be mitigated by the statistical outlier removal (SOR) method [14], which can remove the scattered noise points and abnormal points in the point cloud data.  
In this work, the point cloud upsampling algorithm is applied to improve the accuracy of the experiment. We improve the existing deep learning model using dense graph convolution blocks to make the generated point cloud closer to the real point cloud distribution, called the graph convolution point cloud upsampling generative adversarial network (GPU-GAN). In this paper, a new discriminator model is designed, which gives the model better anti-noise ability by learning the features between points and points in the coordinate space neighborhood. Before using the deep learning model, we use the SOR and pass-through filter [15] to preprocess the point cloud in order to reduce noise interference and improve the operation speed. We evaluate the effectiveness of GPU-GAN by analyzing upsampled point cloud data as well as X-ray diffraction experiments.