# A Survey on Deep Learning in 3D CAD Reconstruction
## 4 Reconstruction of CAD Models from Other Forms  
CAD modeling describe the process of users creating 3D shapes, which are very different from meshes and point clouds and are widely used in many industrial engineering design fields. With recent development, CAD models have been widely adopted as a standard means of shape description in most industrial manufacturing processes. The generation of 3D CAD models faces the challenges in the CAD operation sequence and irregular structure.  
[PERSON] et al. [10] proposed the first 3D generative model that describes shapes as a series of computer-aided design (CAD) operations. They found a representation of irregularities in coordinated CAD models. The deep generative model of CAD proposed by the authors is a series of operations used in CAD tools to construct 3D shapes. They can be directly converted to other shape formats such as point clouds [79] and polygonal meshes [80]. To improve the controllability and hierarchical expression ability of the generation process, [PERSON] et al. [81] introduced a hierarchical neural encoder (HNC) architecture to achieve more refined and structured CAD model sequence generation. At the same time, [PERSON] et al. [26] designed an end-to-end method (SecAD-Net) that can automatically infer 2D sketches and their corresponding 3D extrusion parameters from the input original geometry, thereby realizing the reconstruction of CAD models. In addition, [PERSON] et al. [82] proposed CAPRI-Net, which adaptively learns and assembles parametric geometries from 3D surface data to generate CAD models. This approach provides new insights into CAD model generation.  
[PERSON] et al. [83] proposed CSGNet, which takes voxelized shapes as input and learns to generate a sequence of Constructive Solid Geometry (CSG) operations. In contrast, [PERSON] et al. [10] designed an autoencoder architecture that outputs modeling results expressed as a sequence of CAD operations.  
[PERSON] et al. [84] proposed BrepGen, a diffusion-based generative framework [85] that directly generates CAD models in the form of boundary representations (B-reps). B-reps are more challenging to generate [86] than CSG or sketch representations, mainly because they contain a variety of parameterized surfaces and curves, and each type of geometric element has its own definition and parameter configuration. To form a closed solid, the topological relationship between all geometric shapes must remain correct. Therefore, the generation is very complicated. Other CAD generation models, therefore, use parametric modeling operations and extend two-dimensional cross-sections to operate without the need for generation through B-rep. The emergence of a system that can directly generate boundary representations will have a great impact, reducing a lot of manual labor for skilled designers, reducing the dependence of CAD on professional CAD software, and changing the overall CAD workflow. Therefore, the emergence of BrepGen will promote the goal of building an automatic system that can directly generate B-rep.  
Generating CAD models from voxels and geometric shapes shows potential in operation sequence modeling and direct generation of B-rep models. However, there are still deficiencies in generalization and interpretability [82, 87]. In the future, by introducing structural priors, multimodal fusion, and design rule constraints, the model's ability to express complex shapes can be improved, and the connection with the CAD tool chain can be strengthened to promote the implementation of the model in actual engineering design.