# A Survey on Deep Learning in 3D CAD Reconstruction
## 3 Reconstructing CAD Models from Sketches  
In the traditional method of converting a sketch to a CAD model, the designer first completes the sketch, and then converts the design sketch into a 3D CAD model through a high-precision and editable CAD tool. Due to the involvement of multiple professional skills, this process is often inefficient for design iterations and requires considerable time and resources [64]. Therefore, the research on generating CAD models through sketches is of great significance [65; 66].  
In recent research, [PERSON] et al. [67] proposed a sketch-based CAD modeling system, Sketch2 CAD. By inferring CAD operations from sketch operations, an approximate sketch of man-made objects can be converted into a regular CAD model [68]. For users, the sketch strokes drawn by contextual users are mapped to regular CAD models, helping them to avoid browsing the complex CAD system interface [69]. However, there is also a limitation where this cannot achieve the same modeling level as modern CAD software, even though it can enable sketch designers to quickly generate CAD models. This study provides a reference for future research and promotes related research on converting sketches to CAD models.  
If the process of automatically converting 2D CAD sketches into 3D CAD models can be achieved, it is expected to significantly improve the overall efficiency from design to practical application and greatly reduce costs [70]. Based on this demand, [PERSON] et al. [31] proposed a system that uses vector line drawings as input to generate structured CAD models. This method uses three orthographic projections to reconstruct a complete 3D CAD model, and, especially when the input contains significant noise or missing information, it exhibits better performance than traditional methods [71; 72]. This study effectively addresses a long-standing challenge in the CAD field: reconstructing 3D geometry from multiple orthographic views [73; 74]. Researchers found that two key factors significantly improved the reconstruction effect: first, compared with directly establishing the mapping relationship between the two-dimensional and the three-dimensional, the introduction of an attention mechanism can enhance the model's adaptability to incomplete inputs [75]; second, injecting professional domain knowledge into the generation framework can improve the output quality and enhance its practical value in subsequent tasks. This discovery provides strong theoretical support for subsequent related research. However, this method has not yet integrated the additional information commonly found in CAD drawings, such as layers, annotations, symbols, and text [76; 77], which limits its practicality to some extent.  
In recent years, research on the conversion process from a sketch to a CAD model has achieved some initial results in terms of robustness and generation quality. However, existing methods still have shortcomings in terms of multi-view information fusion, drawing semantics, and geometric structure consistency [78]. In the future, we can integrate layers, symbol semantics, and multimodal input to build a unified sketch-to-CAD framework, and introduce a user interaction mechanism to improve the system's intelligent modeling capabilities and actual efficiency, which will promote the design process to develop towards intelligence.