A Novel Computational Paradigm for Reconstructing Solid CAD Features from a Segmented Manifold Triangular Mesh
Source abstract
Abstract
We introduce a novel computational paradigm for reconstructing solid computer-aided design (CAD) features from the surface of a segmented manifold triangular mesh. This paradigm addresses the challenge of capturing high-level design semantics for manifold triangular meshes and facilitates parametric and variational design capabilities. We categorize four prevalent features, namely extrusion, rotation, sweep, and loft, as generalized swept bodies driven by cross-sectional sketches and feature paths, providing a unified mathematical representation for various feature types. The numerical optimization-based approach conducts geometric processing on the segmented manifold triangular mesh patch, extracting cross-sectional sketch curves and feature paths from its surface, and then reconstructing appropriate features using the Open CASCADE kernel. We employ the personalized three-dimensional (3D) printed model as a case study. Parametric and variant designs of the 3D-printed models are achieved through feature reconstruction of the manifold triangular mesh obtained via 3D scanning.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: 3D Shape Modeling and Analysis · Manufacturing Process and Optimization · Additive Manufacturing and 3D Printing Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science, South-Central Minzu University, Wuhan 430074, China
Hubei Digital Manufacturing Key Laboratory, Wuhan University of Technology, Wuhan 430070, China
Funding
National Natural Science Foundation of China · South-Central Minzu University
References (45)
Show source-provided references
1. From reverse engineering to shape engineering in mechanical design10.1016/j.cirp.2016.04.052 ↗
2. A similarity-based approach for the variant design of core-pulling mechanism in injection mold design10.1007/s00170-021-06962-2 ↗
3. Knot Optimization for Biharmonic B-splines on Manifold Triangle Meshes10.1109/TVCG.2016.2605092 ↗
4. Lightweight Mesh File Format Using Repetition Pattern Encoding for Additive Manufacturing10.1016/j.cad.2020.102914 ↗
5. Liu, Q., Xu, S., Xiao, J., and Wang, Y. (2023). Sharp Feature-Preserving 3D Mesh Reconstruction from Point Clouds Based on Primitive Detection. Remote Sens., 15.10.3390/rs15123155 ↗
6. Parametric CAD modeling: An analysis of strategies for design reusability10.1016/j.cad.2016.01.003 ↗
7. XVoxel-Based Parametric Design Optimization of Feature Models10.1016/j.cad.2023.103528 ↗
8. Digital twin modeling10.1016/j.jmsy.2022.06.015 ↗
9. 3D Systems Inc. (2024, May 01). Geomagic Design X. Available online: https://www.3dsystems.com/software/geomagic-design-x.
10. A Survey of Surface Reconstruction from Point Clouds10.1111/cgf.12802 ↗
11. Efficient RANSAC for Point-Cloud Shape Detection10.1111/j.1467-8659.2007.01016.x ↗
12. Variational shape approximation10.1145/1015706.1015817 ↗
13. Variational shape approximation of point set surfaces10.1016/j.cagd.2020.101875 ↗
14. Applying geometric constraints for perfecting CAD models in reverse engineering10.1016/j.gmod.2015.06.002 ↗
15. GlobFit: Consistently fitting primitives by discovering global relations10.1145/2010324.1964947 ↗
16. Nan, L., and Wonka, P. (2017, January 22–29). PolyFit: Polygonal Surface Reconstruction from Point Clouds. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.10.1109/ICCV.2017.258 ↗
17. Algorithms for reverse engineering boundary representation models10.1016/S0010-4485(01)00100-2 ↗
18. Recognizing geometric primitives in 3D point clouds of mechanical CAD objects10.1016/j.cad.2023.103479 ↗
19. Li, L., Sung, M., Dubrovina, A., Yi, L., and Guibas, L.J. (2019, January 15–20). Supervised Fitting of Geometric Primitives to 3D Point Clouds. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00276 ↗
20. Sharma, G., Liu, D., Maji, S., Kalogerakis, E., Chaudhuri, S., and Měch, R. (2020). ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds, Springer.10.1007/978-3-030-58571-6_16 ↗
21. Yan, S., Yang, Z., Ma, C., Huang, H., Vouga, E., and Huang, Q. (2021, January 10–17). HPNet: Deep Primitive Segmentation Using Hybrid Representations. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.10.1109/ICCV48922.2021.00275 ↗
22. Huang, J., Zhang, Y., and Sun, M. (2021, January 10–17). PrimitiveNet: Primitive Instance Segmentation with Local Primitive Embedding under Adversarial Metric. Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.10.1109/ICCV48922.2021.01506 ↗
23. Automatic CAD model reconstruction from multiple point clouds for reverse engineering10.1115/1.1529210 ↗
24. A comprehensive process of reverse engineering from 3D meshes to CAD models10.1016/j.cad.2013.06.004 ↗
25. Towards locally and globally shape-aware reverse 3D modeling10.1016/j.cad.2011.12.004 ↗
26. A framework for 3D model reconstruction in reverse engineering10.1016/j.cie.2012.07.009 ↗
27. Feature-Based Solid Model Reconstruction10.1115/1.4023129 ↗
28. Generative Design Conversion to Editable and Watertight Boundary Representation10.1016/j.cad.2019.05.016 ↗
29. Boundary conforming mesh to T-NURCC surface conversion10.1016/j.cag.2019.05.012 ↗
30. CAD-based robot path planning and simulation using OPEN CASCADE10.1016/j.procs.2018.07.119 ↗
31. Feature-based modeling for variable fractal geometry design integrated into CAD system10.1016/j.aei.2023.102006 ↗
32. Su, Q., Bi, B., Zhang, P., Shen, L., Huang, X., and Xin, Q. (2022). GPR Image Clutter Suppression Using Gaussian Curvature Decomposition in the PCA Domain. Remote Sens., 14.10.3390/rs14194879 ↗
33. (2024, July 01). Principal Curvature. Available online: https://en.wikipedia.org/wiki/Principal_curvature.
34. Curvature estimation for meshes based on vertex normal triangles10.1016/j.cad.2011.06.006 ↗
35. Taubin, G. (1995, January 20–23). Estimating the tensor of curvature of a surface from a polyhedral approximation. Proceedings of the IEEE International Conference on Computer Vision, Cambridge, MA, USA.
36. PCS: Prominent Cross-Sections for Mesh Models10.3722/cadaps.2010.601-620 ↗
37. B-spline curve fitting based on adaptive curve refinement using dominant points10.1016/j.cad.2006.12.006 ↗
38. A weighted total least-squares algorithm for fitting a straight line10.1088/0957-0233/18/11/025 ↗
39. Spatial arc fitting optimization method based on Lagrangian multiplier method
40. Application of Cubic B-Spline Curve in Computer-Aided Animation Design10.14733/cadaps.2021.S1.43-52 ↗
41. A non-rigid cluster rewriting approach to solve systems of 3D geometric constraints10.1016/j.cad.2009.03.003 ↗
42. A Workbench for Geometric Constraint Solving10.3722/cadaps.2008.471-482 ↗
43. Koch, S., Matveev, A., Jiang, Z., Williams, F., Artemov, A., Burnaev, E., Alexa, M., Zorin, D., and Panozzo, D. (2019, January 15–20). ABC: A Big CAD Model Dataset for Geometric Deep Learning. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.10.1109/CVPR.2019.00983 ↗
44. Paviot, T., and Feringa, J. (2024, May 01). Pythonocc; 3D CAD/CAE/PLM Development Framework for the Python Programming Language. Available online: https://github.com/tpaviot/pythonocc.
45. Efficient Way of Web Development Using Python and Flask