We present a complete system for the purpose of automatically assembling 3D pots given 3D measurements of their fragments commonly called sherds. A Bayesian approach formulated which, at present, models the data given a set sherd geometric parameters. Dense sherd measurement is obtained by scanning the outside surface of each with a laser scanner. Mathematical models, specified by a set of geometric parameters, represent the sherd surface and break curves on the outer surface (where sherds have broken apart). Optimal alignment of assemblies sherds, called configurations, is implemented as maximum likelihood estimation (MLE) of the surface and curve parameters given the measured sherd data for sherds in a configuration. 

In this project, we are developing an object recognition and segmentation framework that uses a shock graph based shape model. Our fragment-based generative model is capable of generating a wide variation of shapes as instances of a given object category. In order to recognize and segment objects, we make use of a progressive selection mechanism to search among the generated shapes for the category instances that are present in the image. The search begins with a large pool of candidates identified by the dynamic programming (DP) algorithm and progressively reduces it in size by applying series of criteria.