Recognition and reconstruction of 3D objects using model-based perceptual grouping

We address a new algorithm for recognition and reconstruction of 3D polyhedral objects, based on perceptual grouping and graph search technique. Perceptual grouping is performed in a model-based framework, in which decision tree classifier is employed for learning and retrieving geometric information of the 3D model object. On the other hand, in order to extract the polygonal patch structure, initial grouping result is represented by a Gestalt graph. Polygonal patch hypotheses are then generated by graph search and verified by the consistency test with the model. In the experiments, it is shown that the model-based grouping reduces the number of the generated hypotheses efficiently, and furthermore, robust recognition and reconstruction are achieved by means of the graph search technique