# A Survey on Deep Learning in 3D CAD Reconstruction
## 2 Reconstruction from Point Cloud to CAD Model  
The reverse engineering of original geometry to a CAD model a fundamental task [33]. The reconstruction from the point cloud to the CAD model is part of this process. The CAD model is in the form of a series of extruded columns, a 2D sketch plus an extruded axis and range, and their combination through Boolean operations [30]. Boolean operations are a type of geometric operation used to combine multiple entities through operations such as union, intersection, and difference [34]. To reconstruct a CAD model through point cloud reverse engineering, you first need to obtain the point cloud through a 3D sensor, then decompose it into primitives or surfaces, and then use some existing shape modelers for further analysis [35; 36; 37]. In recent years, a variety of analysis and modeling methods have been studied for the processing and geometric reconstruction of point clouds in complex scenes [38; 39; 40; 41]. In the future, new ideas may be brought to reconstruction from point clouds to CAD models. From the perspective of CAD users, the entire modeling process usually starts with sketching. First, a two-dimensional sketch is drawn which is a closed curve. Then, it is stretched into a three-dimensional entity [10]. After that, these three-dimensional shapes can be combined through operations such as union, intersection, and difference. For reverse engineering point clouds into primitives usable in CAD modeling [42; 43], the traditional approach is to first convert the point cloud into a mesh, then generate a boundary representation [44] through a series of operations, and then infer that the input can generate the boundary representation in the CAD program [45]. DeepCAD [10] introduced a Transformer-based method to generate complete 3D CAD models, providing a technical direction for subsequent applications related to point cloud modeling. [PERSON] et al. [30] proposed a supervised learning framework (point2 cy) that can convert raw 3D point cloud data into a set of cylinders with extrusion parameters, making it possible to reconstruct CAD models from point clouds and supporting user edits of the reconstruction results. This method regards the modeling of extruded cylinders as a parameterized geometric unit, which is used to jointly express multiple sketch extrusion processes, and has demonstrated its applicability in CAD modeling and geometric representation [46; 47]. [PERSON] et al. [48] proposed PVDConv, a neural module for reconstructing structured CAD configurations from dense voxel or point cloud features. [PERSON] et al. [49] proposed PS-CAD, which reconstructs CAD modeling sequences from point cloud data through an autoregressive model and uses local geometric information as guidance [50]. However, it lacks design rule constraints between different sketch extrusions. Figure 3: A taxonomy of CAD model reconstruction methods. [PERSON] et al. [13] proposed ComplexGen to reconstruct high-quality B-rep CAD models from input point clouds or implicit representations. In recent research, [PERSON] et al. [51] proposed a network architecture (PC2 WF) that can directly map 3D point clouds to vector wireframes, which are composed of 3D junctions and the edges connecting them, representing the structural outline of the object [52]. The reconstructed wireframes can drive and help create 3D CAD models. Wireframes save storage space, are easy to edit in various operations in CAD software (e.g., AutoCAD 2024 or similar), and can be applied in many fields such as part design and manufacturing and CAD model creation [53; 54]. For CAD reconstruction in specific applications, such as in piping systems and building facilities, some studies focused on reconstructing geometric CAD models from sparse or noisy point clouds [55; 56]. For example, [PERSON] et al. [57] used deep learning-based segmentation and BIM-driven alignment techniques to reconstruct pipeline structures from 3D point cloud data with poor scanning quality.