# An Advanced Vehicle Body Part Inspection Scheme Based on Scattered Point Cloud Data
## Related Work  
### Preprocessing of Point Clouds  
For point cloud denoising, [PERSON] et al. applied a bilateral filtering algorithm based on image denoising to the PCD filtering process [5]. In 2005, [PERSON] et al. used the moving least squares (MLS) method to construct a point cloud neighborhood and then designed a forward search paradigm for PCD smoothing, which achieved good results [6]. [PERSON] et al. first removed abnormal points through statistical filtering and then filtered and smoothed the point cloud using an improved tri-lateral filtering algorithm [7]. [PERSON] et al. used unsupervised learning to denoise large-scale point clouds and achieved good results [8]. [PERSON] et al. used the double-tensor voting algorithm to detect the features of PCD and combined Ransac and the multi-normal strategy to carry out point relocation to complete the point cloud denoising [9]. [PERSON] et al. designed a non-iterative double-threshold de-noising process based on the Canny detection algorithm for image processing [10]. This algorithm performed phased de-noising of the point cloud through the threshold and improved the denoising efficiency. For the point cloud simplification process, [PERSON] et al. introduced a triangular grid into the point cloud processing process and then simplified the grid using a vector weighting algorithm to complete the point cloud simplification process [11]. [PERSON] et al. simplified the point cloud data through the geometric information of parts and validated the algorithm by using two sets of part PCD [12]. [PERSON] et al. designed an adaptive point cloud simplification strategy and the experiment proved the accuracy and efficiency of the algorithm [13]. [PERSON] et al. used the octree to construct the topological relationship between points and designed different sampling strategies for edge points and non-edge points, respectively, to simplify the point cloud data [14]. The test set was analyzed and verified. [PERSON] et al. introduced the improved particle swarm optimization algorithm to the average distance method in the simplified point cloud algorithm and applied the algorithm to the online measurement of the 3D scanning of the granary [15]. The experiment shows the effectiveness of the algorithm. For the point cloud registration process, [PERSON] et al. provided a point cloud registration method based on different levels for a measurement scheme of multiple sensors. This method registers the data according to the accuracy requirements of the point cloud and improves the registration accuracy and efficiency of the point cloud [16]. [PERSON] et al. designed a point cloud registration algorithm based on spherical feature constraints for curved point cloud data. By constructing virtual overlapping areas, the point cloud movement parameters were calculated and the effectiveness of the algorithm was verified by experiments [17]. [PERSON] used deep learning to register three-dimensional images with noise and the effectiveness of the algorithm was proven through experiments [18]. [PERSON] et al. first carried out a rough registration of point clouds using a weighted \(l_{q}\)-norm and then completed the process of fine registration by using topology diagram theory. This algorithm is considered to have good stability [19]. Based on the registration algorithm of the fast point feature histogram, [PERSON] optimized the neighborhood density of points to complete the coarse registration process and ultimately carried out fine registration with ICP to improve the registration accuracy of the point cloud [20]. It can be seen from the relevant literature that there are various pre-processing methods for point clouds. For point cloud denoising algorithm, although bilateral filtering has a great advantage in feature retention, it needs to calculate the feature information of point cloud. When the noise points are relatively dense, the error cannot be controlled. However, clustering and other algorithms cannot effectively identify target point cloud and outlier groups.