Applying geometric constraints for perfecting CAD models in reverse engineering

Reverse engineered CAD objects get perfected using constrained fitting.Various local and global constraints are detected and enforced upon segmented data.The selection of meaningful subsets of "likely" constraints is an automatic process.Best alignments, reference grids and optimal symmetry planes are computed.Special measures for partial and approximate constraint satisfaction are proposed. An important area of reverse engineering is to produce digital models of mechanical parts from measured data points. In this process inaccuracies may occur due to noise and the numerical nature of the algorithms, such as, aligning point clouds, mesh processing, segmentation and surface fitting. As a consequence, faces will not be precisely parallel or orthogonal, smooth connections may be of poor quality, axes of concentric cylinders may be slightly tilted, and so on. In this paper we present algorithms to eliminate these inaccuracies and create "perfected" B-rep models suitable for downstream CAD/CAM applications.Using a segmented and classified set of smooth surface regions we enforce various constraints for automatically selected groups of surfaces. We extend a formerly published technology of Benk? et?al. (2002). It is an essential element of our approach, however, that we do not know in advance the set of surfaces that will actually get involved in the final constrained fitting. We propose local methods to select and synchronize "likely" geometric constraints, detected between pairs of entities. We also propose global methods to determine constraints related to the whole object, although the best-fit coordinate systems, reference grids and symmetry planes will be determined only by surface entities qualified as relevant. Lots of examples illustrate how these constrained fitting algorithms improve the quality of reconstructed objects. Display Omitted