Foundation models in reverse engineering, CAD reconstruction and additive manufacturing: A systematic review
Reverse engineering is undergoing a profound transformation driven by the emergence of foundation models. These models, together with their applications, are progressively extending the limits of artificial intelligence (AI) from geometrical reconstruction to a more sophisticated level engineering reasoning. In contrast to traditional methods, which usually use sequences of algorithms for specific 3D retrieval and reconstruction tasks, current foundation models train geometrical representations that are able to support semantic reasoning, computer-aided design (CAD) reconstruction, and intelligent manufacturing in a unified way. This work provides an overview of foundation models within the context of reverse engineering and their combination with CAD reconstruction and additive manufacturing. Starting from a systematic bibliometric analysis of the recent literature, the present review is mainly focused on the development of representation learning and reviews the key geometric forms that are being used by the current foundation models. The paper also explores the engineering abilities facilitated by such models in the reverse engineering process. Furthermore, current challenges, research obstacles, and future directions are critically explored, emphasizing the recent transition toward intelligent engineering systems. The analysis shows that foundation models are gradually making reverse engineering move from being an assortment of discrete computational activities to becoming an engineering process workflow that is made possible by means of geometry representations and multimodal reasoning as well as engineering AI. Finally, considerations on the importance of developing engineering conceptual frameworks to interpret the emerging role of foundation models in future intelligent engineering systems are drafted.
- Várady T, Martin RR, Cox J. Reverse engineering of geometric models—an introduction. Comput Aided Des. 1997;29(4):255-268. doi: 10.1016/S0010-4485(96)00054-1
- Buonamici F, Carfagni M, Furferi R, Governi L, Lapini A, Volpe Y. Reverse engineering modeling methods and tools: a survey. Comput Aided Des Appl. 2018;15(3):443-464. doi: 10.1080/16864360.2017.1397894
- Pomerleau F, Colas F, Siegwart R. A review of point cloud registration algorithms for mobile robotics. Found Trends Robot. 2015;4(1):1-104. doi: 10.1561/2300000035
- Berger M, Tagliasacchi A, Seversky LM, et al. A survey of surface reconstruction from point clouds. Comput Graph Forum. 2017;36(1):301-329. doi: 10.1111/cgf.12802
- Grilli E, Menna F, Remondino F. A review of point clouds segmentation and classification algorithms. Int Arch Photogramm Remote Sens Spatial Inf Sci. 2017;XLII-2/W3:339-344. doi: 10.5194/isprs-archives-XLII-2-W3-339-2017
- Qi CR, Su H, Mo K, Guibas LJ. PointNet: deep learning on point sets for 3D classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). July 21-26, 2017; Honolulu, HI. 2017:77-85. doi: 10.1109/CVPR.2017.16
- Qi CR, Yi L, Su H, Guibas LJ. PointNet++: Deep hierarchical feature learning on point sets in a metric space. arXiv. Preprint posted online 2017. doi: 10.48550/arXiv.1706.02413
- Wang Y, Sun Y, Liu Z, Sarma SE, Bronstein MM, Solomon JM. Dynamic graph CNN for learning on point clouds. ACM Trans Graph. 2019;38(5):1-12. doi: 10.1145/3326362
- Engel N, Belagiannis V, Dietmayer K. Point Transformer. IEEE Access. 2021;9:134826-134840. doi: 10.1109/ACCESS.2021.3116304
- Bommasani R, Hudson DA, Adeli E, et al. On the opportunities and risks of foundation models. arXiv. Preprint posted online 2021. doi: 10.48550/arXiv.2108.07258
- Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16x16 words: transformers for image recognition at scale. arXiv. Preprint posted online 2020. doi: 10.48550/arXiv.2010.11929
- Radford A, Kim JW, Hallacy C, et al. Learning transferable visual models from natural language supervision. In: Proceedings of the 38th International Conference on Machine Learning. July 18-24, 2021; Online. 2021;139:8748-8763.
- Yu X, Tang L, Rao Y, Huang T, Zhou J, Lu J. Point-BERT: Pre-training 3D Point Cloud Transformers with Masked Point Modeling. In: Proceedings of the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 18-24, 2022; New Orleans, LA. IEEE; 2022:19291-19300. doi: 10.1109/cvpr52688.2022.01871
- Pang Y, Wang W, Tay FEH, Liu W, Tian Y, Yuan L. Masked Autoencoders for Point Cloud Self-supervised Learning. In: Lecture Notes in Computer Science (Computer Vision – ECCV 2022). Cham, Switzerland: Springer Nature Switzerland; 2022:604-621. doi: 10.1007/978-3-031-20086-1_35
- Kirillov A, Mintun E, Ravi N, et al. Segment Anything. In: Proceedings of the 2023 IEEE/CVF International Conference on Computer Vision (ICCV). October 1-6, 2023; Paris, France. IEEE; 2023:3992-4003. doi: 10.1109/iccv51070.2023.00371
- Xu R, Wang X, Wang T, Chen Y, Pang J, Lin D. PointLLM: Empowering Large Language Models to Understand Point Clouds. In: Computer Vision – ECCV 2024 (Lecture Notes in Computer Science). Cham, Switzerland: Springer Nature Switzerland; 2024:131-147. doi: 10.1007/978-3-031-72698-9_8
- Ngo TD, Kashani A, Imbalzano G, Nguyen KTQ, Hui D. Additive manufacturing (3D printing): a review of materials, methods, applications and challenges. Compos Part B Eng. 2018;143:172-196. doi: 10.1016/j.compositesb.2018.02.012
- Mashhadi AR, Behdad S. Optimal sorting policies in remanufacturing systems: application of product life-cycle data in quality grading and end-of-use recovery. J Manuf Syst. 2017;43(Pt 1):15-24. doi: 10.1016/j.jmsy.2017.02.006
- Zhang J, Zhao X, Chen Z, Lu Z. A review of deep learning-based semantic segmentation for point cloud. IEEE Access. 2019;7:179118-179133. doi: 10.1109/ACCESS.2019.2958671
- Bello SA, Yu S, Wang C, Adam JM, Li J. Deep learning on 3D point clouds: a review. Remote Sens. 2020;12(11):1729. doi: 10.3390/rs12111729
- Tychola KA, Vrochidou E, Papakostas GA. Deep learning-based computer vision under the prism of 3D point clouds: a systematic review. Vis Comput. 2024;40(11):8287-8329. doi: 10.1007/s00371-023-03237-7
- Xiao B, Wu H, Xu W, et al. Florence-2: advancing a unified representation for a variety of vision tasks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 16-22, 2024; Seattle, WA. 2024:4818-4829. doi: 10.1109/CVPR52733.2024.00461
- Malhotra P. The rise of passive investing: a systematic literature review applying the PRISMA framework. J Cap Mark Stud. 2024;8(1):95-125. doi: 10.1108/JCMS-12-2023-0046
- Johnson AE, Hebert M. Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans Pattern Anal Mach Intell. 1999;21(5):433-449. doi: 10.1109/34.765655
- Rusu RB, Blodow N, Marton ZC, Beetz M. Aligning point cloud views using persistent feature histograms. In: Proceedings of the 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems. September 22-26, 2008; Nice, France. IEEE; 2008:3384-3391. doi: 10.1109/iros.2008.4650967
- Rusu RB, Blodow N, Beetz M. Fast Point Feature Histograms (FPFH) for 3D registration. In: 2009 IEEE International Conference on Robotics and Automation. IEEE; 2009:3212-3217. doi: 10.1109/robot.2009.5152473
- Tombari F, Salti S, Di Stefano L. Unique Signatures of Histograms for Local Surface Description. In: Computer Vision – ECCV 2010 (Lecture Notes in Computer Science). Heidelberg, Germany: Springer Berlin Heidelberg; 2010:356-369. doi: 10.1007/978-3-642-15558-1_26
- Wu R, Xiao C, Zheng C. DeepCAD: a deep generative network for computer-aided design models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). October 10-17, 2021; Montreal, Canada. 2021:6752-6762. doi: 10.1109/ICCV48922.2021.00670
- Zhao H, Jiang L, Fu CW, Jia J, Torr PHS, Koltun V. Point Transformer. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). October 10-17, 2021; Montreal, Canada. 2021:16259-16268. doi: 10.1109/ICCV48922.2021.01595
- Pfaff T, Fortunato M, Sanchez-Gonzalez A, Battaglia PW. Learning Mesh-Based Simulation with Graph Networks. arXiv. Preprint posted online 2020. doi: 10.48550/arXiv.2010.03409
- Liang Y, Zhao S, Yu B, Zhang J, He F. MeshMAE: masked autoencoders for 3D mesh data analysis. In: Avidan S, Brostow GJ, Cissé M, Farinella GM, Hassner T, eds. Computer Vision – ECCV 2022. Lecture Notes in Computer Science. Vol 13663. New York, NY: Springer; 2022:37-54. doi: 10.1007/978-3-031-20062-5_3
- Liu M, Zeng C, Wei X, et al. MeshFormer : High-Quality Mesh Generation with 3D-Guided Reconstruction Model. In: Advances in Neural Information Processing Systems 37. San Diego, CA: Neural Information Processing Systems Foundation, Inc. (NeurIPS); 2024:59314-59341. doi: 10.52202/079017-1893
- Lambourne JG, Willis KDD, Jayaraman PK, Sanghi A, Meltzer P, Shayani H. BRepNet: A topological message passing system for solid models. In: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 10-25, 2021; Nashville, TN. IEEE; 2021:12768-12777. doi: 10.1109/cvpr46437.2021.01258
- Jayaraman PK, Lambourne JG, Desai N, Willis KDD, Sanghi A, Morris NJW. SolidGen: An autoregressive model for direct B-rep synthesis. arXiv. Preprint posted online 2022. doi: 10.48550/arXiv.2203.13944
- Liang CX, Tian P, Yin CH, et al. A comprehensive survey and guide to multimodal large language models in vision-language tasks. 2026;14(6):125. doi: 10.3390/computation14060125
- Li C, Gan Z, Yang Z, et al. Multimodal foundation models: from specialists to general-purpose assistants. Found Trends Comput Graph Vis. 2024;16(1-2):1-214. doi: 10.1561/0600000110
- Liu H, Li C, Wu Q, Lee YJ. Visual Instruction Tuning. arXiv. Preprint posted online 2023. doi: 10.48550/arXiv.2304.08485
- Zhan J, Li Y, Guo Z, et al. A 4D-printed NiTi alloy with encoded microstructures evades the cooling capacity-energy efficiency trade-off in elastocaloric refrigeration. Mater Sci Eng R Rep. 2026;169:101191. doi: 10.1016/j.mser.2026.101191
- Zhan J, Li K, Ma R, et al. Achieving fine tailoring of elastocaloric properties of laser powder bed-fused NiTi alloy via laser beam manipulation. Int J Mach Tools Manuf. 2024;202:104210. doi: 10.1016/j.ijmachtools.2024.104210
- Yang Y, Wang B, Su B, et al. Novel in-situ alloying strategy of laser powder bed fusion for fabricating crack-free and composition-homogeneous NiTi alloys. J Mater Sci Technol. 2027;279:21-31. doi: 10.1016/j.jmst.2026.04.057
- Chen Y, Wang Y, Chen Y, et al. Process-microstructure-property relationships and strengthening mechanisms in ultrasonic-magnetic assisted hybrid wire electrical discharge and electrochemical machining of shape memory alloy. Int J Mach Tools Manuf. 2026;220:104423. doi: 10.1016/j.ijmachtools.2026.104423
- Li Y, Zhao H, Jiang H, et al. Large language models for manufacturing. arXiv. Preprint posted online 2024. doi: 10.48550/arXiv.2410.21418
- Zhang H, Semujju SD, Wang Z, et al. Large scale foundation models for intelligent manufacturing applications: a survey. J Intell Manuf. 2026;37(1):119-170. doi: 1007/s10845-024-02536-7
- Rahman MA, Shahrior MF, Iqbal K, Abushaiba AA. Enabling intelligent industrial automation: a review of machine learning applications with digital twin and edge AI integration. 2025;6(3):37. doi: 10.3390/automation6030037
- Wu Q, Bansal G, Zhang J, et al. AutoGen: enabling next-generation LLM applications via multi-agent conversation. arXiv. arXiv:2308.08155, 2023. doi: 10.48550/arXiv.2308.08155
- Koch S, Matveev A, Jiang Z, et al. ABC: A Big CAD Model Dataset for Geometric Deep Learning. In: Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). June 15-20, 2019; Long Beach, CA. IEEE; 2019:9593-9603. doi: 10.1109/cvpr.2019.00983
- Sun Y, Li X, Sha Z. Large language models for computer-aided design fine tuned: dataset and experiments. J Mech Des. 2025;147(4):041710. doi: 10.1115/1.4067713
- Ponnusamy V, Ekambaram D, Zdravkovic N. Artificial intelligence-enabled digital twin technology in smart manufacturing. In: Industry 4.0, Smart Manufacturing, and Industrial Engineering. Boca Raton, FL: CRC Press; 2024:248-270. doi: 1201/9781003473886-13
- Müller HA, Jahnke JH, Smith DB, Storey MA, Tilley SR, Wong K. Reverse engineering. In: Proceedings of the Conference on The Future of Software Engineering. ACM; 2000:47-60. doi: 10.1145/336512.336526
