Extraction of 2D groupings for 3D object recognition
Jean-Luc Arseneault, Robert Bergevin, Denis Laurendeau · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1994
An approach for the recognition of 3D objects from single 2D views is presented. Using perceptual organization, hierarchies of features based on parallelism, co-linearity, and intersection are generated. Our local grouping algorithm is particularly inspired by the formalism defined by Etamadi et al., which is concerned with the formation of self-consistent groupings of straight lines from which all higher level groupings may be derived. Our approach extends this formalism to circular arcs. Surfaces in the scene are then extracted based on perceptual laws of symmetry and closure. The recognition process uses relational graphs of surfaces constructed by establishing the proximity, adjacency, and inclusion relations that exist between the surfaces. We identify closures which can be interpreted as the borders of the visible surfaces of objects and can also be used to describe the 2D shape of the surfaces. We show that a graph can be constructed from the relations between these closures and that similarities can be extracted from two different graphs obtained by analyzing two views of the same scene. Typical results obtained for complex indoor scenes are presented.