The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Parametric and Feature Based CAD/Cam: Concepts, Techniques, and Applications
Parametric and Feature Based CAD/Cam: Concepts, Techniques, and Applications
Constrained fitting in reverse engineering
Computer Aided Geometric Design
Erep: An editable, high-level representation for geometric design and analysis
Selected and Expanded Papers from the IFIP TC5/WG5.2 Working Conference on Geometric Modeling for Product Realization
Simultaneous shape decomposition and skeletonization
Proceedings of the 2006 ACM symposium on Solid and physical modeling
Feature-based 3D morphing based on geometrically constrained sphere mapping optimization
Proceedings of the 2010 ACM Symposium on Applied Computing
Comparative study of different digitization techniques and their accuracy
Computer-Aided Design
SMI 2011: Full Paper: Parallel computation of spherical parameterizations for mesh analysis
Computers and Graphics
Feature-based 3D morphing based on geometrically constrained spherical parameterization
Computer Aided Geometric Design
A parametric feature-based approach to reconstructing traditional filigree jewelry
Computer-Aided Design
Towards locally and globally shape-aware reverse 3D modeling
Computer-Aided Design
A complete framework for 3D mesh morphing
Proceedings of the 11th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and its Applications in Industry
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Reverse engineering, the process of obtaining a geometric CAD model from measurements obtained by scanning an existing physical model, is widely used in numerous applications, such as manufacturing, industrial design and jewellery design. In this work we propose a framework for reverse engineering objects of freeform design to obtain a fully editable feature-based CAD model that can be reproduced or modified before production. We focus on the process of detecting features on a cloud point and we present a fast method for analyzing the morphology of the surface defined by the point cloud. We compute a point wise characteristic called point concavity intensity and we use this quantity to detect regions that are then refined to object features. The proposed algorithm takes overall O(nlogn) time, where n is the cardinality of the point cloud.