Recognizing geometric patterns for beautification of reconstructed solid models

Boundary representation models reconstructed from 3D range data suffer from various inaccuracies caused by noise in the data and the model building software. The quality of such models can be improved in a beautification step, which finds regular geometric patterns approximately present in the model and imposes a maximal consistent subset of constraints deduced from these patterns on the model. This paper presents analysis methods seeking geometric patterns defined by similarities. Their specific types are derived from a part survey estimating the frequencies of the patterns in simple mechanical components. The methods seek clusters of similar objects which describe properties effaces, loops, edges and vertices, try to find special values representing the clusters, and seek approximate symmetries of the model. Experiments show that the patterns detected appear to be suitable for the subsequent beautification steps.