# A Survey on Deep Learning in 3D CAD Reconstruction
###### Abstract  
Three-dimensional CAD reconstruction is a long-standing and important task in fields such as industrial manufacturing, architecture, medicine, film and television, research, and education. Reconstructing CAD models remains a persistent challenge in machine learning. There have been many studies on deep learning in the field of 3D reconstruction. In recent years, with the release of CAD datasets, there have been more and more studies on 3D CAD reconstruction using deep learning. With the continuous deepening of research, deep learning has significantly improved the performance of tasks in the field of CAD reconstruction. However, this task remains challenging due to data scarcity and labeling difficulties, model complexity, and lack of generality and adaptability. This paper reviews both classic and recent research results on 3D CAD reconstruction tasks based on deep learning. To the best of our knowledge, this is the first investigation focusing on the CAD reconstruction task in the field of deep learning. Since there are relatively few studies related to 3D CAD reconstruction, we also investigate the reconstruction and generation of 2D CAD sketches. According to the different input data, we divide all investigations into the following categories: point cloud input to 3D CAD models, sketch input to 3D CAD models, other input to 3D CAD models, reconstruction and generation of 2D sketches, characterization of CAD data, CAD datasets, and related evaluation indicators. Commonly used datasets are outlined in our taxonomy. We provide a brief overview of the current research background, challenges, and recent results. Finally, future research directions are discussed. CAD; deep learning; 3D reconstruction; literature survey +
Footnote †: journal: Medicalteaching and research, as shown in Figure 1. The emergence and application of mature CAD reconstruction technology will promote the efficiency and accuracy of various industries in the future, and foster innovation and progress in the industry. Traditional CAD 3D reconstruction methods rely on manual tool operations. Although these methods are effective in simple scenarios, they show obvious limitations when facing complex geometric structures. Manual tool operations often require experienced experts for adjustments and optimizations, have a low degree of automation, and cannot be adapted to large-scale industrial application needs. With the emergence of large datasets of CAD representations in recent years, researchers in machine learning and deep learning have begun to study CAD data [7; 8; 9; 10], as shown in Figure 2. Previous studies have used these datasets for many research applications, such as segmentation, classification, and assembly [11; 12]. In order to solve the problems of traditional CAD 3D reconstruction, deep learning has been introduced into the field of CAD 3D reconstruction in recent years, which has greatly improved the degree of automation of reconstruction and the quality of model generation. With the deepening of the research, a series of deep learning methods has emerged for generating structured CAD data through 2D sketches or 3D models [13; 14; 15]. There is also research on B-rep (boundary representation), which is used to generate focused, unconstrained CAD models. B-rep is one form of CAD data [16; 17]. Regarding the representation form of CAD, the research on 3D learning of the CSG (Constructive Solid Geometry) representation form has gradually increased [18; 19; 20], pursuing the universality, simplicity, and quality of learning models. As for boundary representation, neural networks were applied to its segmentation task in the 1990s [21; 22; 23; 24], but, due to the lack of datasets and other factors, significant progress was not made until recent years [16; 25]. Recent CAD generation models also use extrusion modeling operations to avoid the direct generation of B-rep [26; 27; 28].