3D shape matching and inspection using geometric features and relational learning

In this paper we consider the problem of matching 3D sensed data with models and inspection for defects where the correspondence between models and data needs to be solved in robust and efficient ways. We explore the use of machine learning (in particular, relational learning) as an efficient method for solving correspondence (and so, pose estimation) as well as automatically generating rules for acceptable shape variations from training data. As an additional but necessary issue, we also consider the use of view-independent covariance methods for the extraction of surface features used to determine shape signatures which correspond to curvature-like surface attributes. Such features are utilized in the relational learning model. (C) 1998 Academic Press.