A Survey on Deep Learning in 3D CAD Reconstruction
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 d...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/12/6681 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849467578701840384 |
|---|---|
| author | Ruiquan Lin Yunwei Ji Wanting Ding Tianxiang Wu Yaosheng Zhu Mengxi Jiang |
| author_facet | Ruiquan Lin Yunwei Ji Wanting Ding Tianxiang Wu Yaosheng Zhu Mengxi Jiang |
| author_sort | Ruiquan Lin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-28a00a2310d14f97be52fed0708e211e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-28a00a2310d14f97be52fed0708e211e2025-08-20T03:26:10ZengMDPI AGApplied Sciences2076-34172025-06-011512668110.3390/app15126681A Survey on Deep Learning in 3D CAD ReconstructionRuiquan Lin0Yunwei Ji1Wanting Ding2Tianxiang Wu3Yaosheng Zhu4Mengxi Jiang5School of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, ChinaSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, ChinaSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, ChinaSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, ChinaSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, ChinaSchool of Advanced Manufacturing, Fuzhou University, Quanzhou 362251, ChinaThree-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.https://www.mdpi.com/2076-3417/15/12/6681CADdeep learning3D reconstructionliterature survey |
| spellingShingle | Ruiquan Lin Yunwei Ji Wanting Ding Tianxiang Wu Yaosheng Zhu Mengxi Jiang A Survey on Deep Learning in 3D CAD Reconstruction Applied Sciences CAD deep learning 3D reconstruction literature survey |
| title | A Survey on Deep Learning in 3D CAD Reconstruction |
| title_full | A Survey on Deep Learning in 3D CAD Reconstruction |
| title_fullStr | A Survey on Deep Learning in 3D CAD Reconstruction |
| title_full_unstemmed | A Survey on Deep Learning in 3D CAD Reconstruction |
| title_short | A Survey on Deep Learning in 3D CAD Reconstruction |
| title_sort | survey on deep learning in 3d cad reconstruction |
| topic | CAD deep learning 3D reconstruction literature survey |
| url | https://www.mdpi.com/2076-3417/15/12/6681 |
| work_keys_str_mv | AT ruiquanlin asurveyondeeplearningin3dcadreconstruction AT yunweiji asurveyondeeplearningin3dcadreconstruction AT wantingding asurveyondeeplearningin3dcadreconstruction AT tianxiangwu asurveyondeeplearningin3dcadreconstruction AT yaoshengzhu asurveyondeeplearningin3dcadreconstruction AT mengxijiang asurveyondeeplearningin3dcadreconstruction AT ruiquanlin surveyondeeplearningin3dcadreconstruction AT yunweiji surveyondeeplearningin3dcadreconstruction AT wantingding surveyondeeplearningin3dcadreconstruction AT tianxiangwu surveyondeeplearningin3dcadreconstruction AT yaoshengzhu surveyondeeplearningin3dcadreconstruction AT mengxijiang surveyondeeplearningin3dcadreconstruction |