# A Survey on Deep Learning in 3D CAD Reconstruction
## 9 Conclusions  
In this paper, we presented a comprehensive overview of deep learning-based CAD reconstruction. We reviewed the reconstruction methods for CAD models developed over the past decade and classify them based on input type: point clouds, sketches, or others. We also summarized various CAD data representations and highlighted recent works to reflect current progress and research trends. Additionally, we introduced relevant public datasets to support reproducibility and benchmark comparisons. This paper focused on key tasks such as point cloud reconstruction, sketch generation, and representation modeling. We conducted a systematic analysis of multiple dimensions, including input modalities, representation methods, modeling strategies, and evaluation metrics. By organizing the research landscape and identifying key findings, we constructed a multi-task knowledge framework that addresses the lack of a unified and comprehensive review in this domain. In terms of data resources, we organized the mainstream public datasets and core evaluation metrics in a structured manner, facilitating better reproducibility and cross-method comparisons. While notable progress has been made in parametric modeling, structural representation, and task integration, challenges remain--such as the scarcity of well-annotated datasets, limited cross-modal modeling, and difficulties in handling complex constraints. To address these issues, we suggest future directions including multi-source data fusion, the incorporation of structural priors, and the enhancement of interactive modeling mechanisms. Emerging technologies such as Transformer architectures, multimodal fusion, and structural prior learning show great promise for improving model controllability, interpretability, and practical applicability. We advocate for future research to focus on building unified frameworks for CAD reconstruction that enhance both system integration and industrial deployment. Overall, this paper not only summarizes the state-of-the-art advances and key challenges, but also provides a clear research roadmap and methodological guidance for advancing deep learning-based 3D CAD reconstruction towards intelligent design systems. **Author Contributions**: Conceptualization, R.L. ; methodology, R.L. and Y.J. ; formal analysis, Y.J. and M.J.; data curation, W.D. and T.W. ; visualization, Y.J. ; writing--original draft, Y.J. ; writing--review and editing, R.L. and Y.J. ; supervision, Y.Z. ; project administration, M.J.; funding acquisition, M.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Enterprise Commissioned Research Project (00852405) and the Fuzhou University Graduate Education and Teaching Reform Project (FYJG2023039). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. * (23) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Boundary representation-based feature identification. In _Manufacturing Research and Technology_; Elsevier: Amsterdam, The Netherlands, 1994; Volume 20, pp. 15-38. * (24) [PERSON]; [PERSON]; [PERSON] Application of neural networks in feature recognition of mould reinforced plastic parts. _Concurr. Eng._**1999**, \(7\), 115-122. [CrossRef]
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