## 1 Introduction
### Related Work
#### 1.2.2 Methods Based on 3D Point Clouds  
From the perspective of learning mechanisms, 3D defect detection methods are classified into traditional and deep learning-based approaches. The former can be further categorized into two types: comparison methods, which detect defects by aligning the defect point cloud with the nominal point cloud and analyzing their differences, and feature-based methods, which rely on carefully crafted handcrafted features for defect detection. [PERSON] et al. [35] aligned the source and target point clouds and employed a patch-based approach to compute features, such as the mean and maximum distances between them, which were then input into a classification model for defect detection. [PERSON] et al. [36] conducted a color space analysis on point clouds, using color space distances as criteria for defect identification. [PERSON] et al. [37] utilized an adaptive iterative closest point (ICP) algorithm to calculate distance deviations, followed by clustering to merge defect regions, and classified defects using a decision tree classifier. Similarly, [PERSON] et al. [38] combined ICP with fast point feature histograms (FPFHs) to compute registration errors, employed an octree structure for rapid defect region localization, and applied region growing to extract complete defects. [PERSON] et al. [39] utilized an enhanced normal rotation projection statistical feature to identify discrepancies between defect and nominal point clouds, extracting potential defects and further refining them using curvature-based Euclidean clustering. Some studies leverage the K-nearest neighbors (KNN) algorithm to compute surface normals and curvatures of point clouds as critical defect identification features, identifying defects by comparing normal vectors or curvature deviations, or by transforming these characteristics into mathematical expressions to serve as inputs for specific filters [40; 41; 42; 43; 44; 45; 9]. Building on this, other methods employ surface or curve fitting techniques, calculating the height difference between defect point clouds and the fitted curves or surfaces as defect features, and subsequently clustering the results to delineate complete defect regions [47; 48; 49; 50; 51]. Notably, certain methods exploit mapping techniques to assign grayscale image values as color features to point clouds, or render 3D point clouds into 2D projections with preserved depth information, facilitating their use in neural network training [52; 53; 54; 55]. [PERSON] et al. [56] introduced PointNet, the first end-to-end network designed explicitly for processing unordered point clouds, paving the way for subsequent advancements, such as PointNet++ [57], Transformer [58], PointConv [59], PointMLP [60], and PointNeXt [61]. These innovative architectures have successfully extended deep learning to the domain of 3D point cloud analysis. Building on the Transformer network, [PERSON] et al. [62] incorporated an enhanced edge convolution mechanism alongside KNN to improve local feature extraction and integration in point clouds. [PERSON] et al. [63] proposed a grouped sampling strategy to extract raw point cloud features, further refining the training process with an adaptive momentum optimizer and a cosine learning rate decay scheduler to achieve superior defect classification and segmentation. [PERSON] et al. [64] developed a dual-stream regional attention network, leveraging backpropagation to focus directly on regions of interest. This approach reduced redundant processing, mitigated dependence on boundary annotations, and significantly enhanced real-time performance. [PERSON] et al. [65] extracted defect features from both 3D and 2D images, employing these features to train a fully convolutional neural network for defect detection. [PERSON] et al. [66] extended the PointNet architecture to extract multi-scale features from point cloud data, employing a cascaded fusion strategy across different layers to enhance defect detection accuracy.