Approximate geometric models, e.g. as created by reverse engineering, describe the approximate shape of an object, but do not record the underlying design intent. Automatically inferring geometric aspects of the design intent, represented by feature trees and geometric constraints, enhances the utility of such models for downstream tasks. One approach to design intent detection in such models is to decompose them into regularity features. Geometric regularities such as symmetries may then be sought in each regularity feature, and subsequently be combined into a global, consistent description of the model’s geometric design intent. This paper describes a systematic approach for finding such regularity features based on recovering broken symmetries in the model. The output is a tree of regularity features for subsequent use in regularity detection and selection. Experimental results are given to demonstrate the operation and efficiency of the algorithm.

Reverse engineering is the process of obtaining a geometric CAD model from 3D points acquired by scanning an existing physical model. It is widely used in numerous applications, such as manufacturing, industrial design and jewelry design and reproduction. We argue that for creating editable CAD models meant for manufacturing it is more appropriate to use feature-based constraint-based representations, since they capture design intent. We provide a framework for reverse engineering of small objects and in particular jewelry that combines cross section identification, feature and constraint information exploitation to attain robust, accurate and editable CAD models. First, we extract certain candidate features for describing our point cloud. These features are then reconstructed to describe the solid object. Constraints are automatically detected and maintained. Constraints capture design intent and provide robustness guaranties. Voxel inspired techniques are also employed to describe repeated patterns common to various types of traditional jewelry.