# A Survey on Deep Learning in 3D CAD Reconstruction
## 5 Design and Generation of CAD Sketches  
Since there are relatively few studies on 3D CAD reconstruction, we also investigated the reconstruction and generation of 2D CAD sketches.  
In computer-aided design (CAD), a common method is to extrude and combine two-dimensional sketches to obtain complex three-dimensional models. The essence of a two-dimensional CAD model is a collection of primitives (such as lines and arcs) combined by a set of relationships (such as collinearity, parallelism, and tangents) [88, 89]. A two-dimensional sketch is formed into a two-dimensional complex shape through a series of CAD operations.  
When using training models to automatically generate CAD sketches, there are challenges in the complexity of the graph and the heterogeneity of the model [87, 90]. [PERSON] et al. [91] proposed the SketchGen framework, which solved the problem of heterogeneity by designing a sequential language of primitives and constraints. The SketchGen framework is used for the automatic and complete generation of CAD sketches. It includes primitives and constraints. However, the SketchGen model has the defect of autoregressive characteristics that prevents error correction in the early part of the sequence.  
In CAD models, two-dimensional sketches structured within three-dimensional structures are difficult to manufacture. [PERSON] et al. [92] proposed a machine learning model for automatic sketch generation. By combining general language modeling techniques with existing data serialization protocols, the generation of complex structured objects is achieved.  
For parametric sketches, [PERSON] et al. [93] proposed a method for modeling and synthesizing parametric CAD sketches. A generative model was used to synthesize CAD sketches using autoregressive sampling of geometric primitives and constraints. This model is expected to help mechanical design work by synthesizing realistic CAD sketches.  
For engineering sketches, [PERSON] et al. [32] proposed generative models for engineering sketches, CurveGen and TurtleGen. Both can generate curve primitives and generate realistic engineering sketches without using sketch constraint solvers. The generated model contains topological information to assist in the task of constraint and 3D CAD modeling.  
Another potential approach in the generation of engineering sketches is program synthesis: using neural network and programming language technology to generate or infer programs representing geometric shapes [83].  
In terms of CAD sketch generation, the existing research has improved its expressive capabilities through primitive modeling, constraint sequence, and program synthesis. However, due to the weak error correction ability of the model and insufficient modeling of constraint relationships, the accuracy and engineering usefulness of the generated sketchesare still limited [94; 95]. In the future, the combination of graph neural networks, constraint solving mechanisms, and language guidance can improve the controllability, structural integrity, and system integration of the generated model, increasing its practical value in engineering design scenarios.