Simultaneous Recognition and Modeling for Learning 3-D Object Models From Everyday Scenes

Object recognition and modeling have classically been studied separately, but practically, they are two closely correlated aspects. In this paper, by exploring the interrelations, we propose a framework to address these two problems at the same time, which we call simultaneous recognition and modeling. Differing from traditional recognition process which consists of off-line object model learning and on-line recognition procedures, our method is solely online. Starting with an empty object database, we incrementally build up object models while at the same time using these models to identify newly observed object views. In the proposed framework, objects are modeled as view graphs and a probabilistic observation model is presented. Both the appearance and the spatial structure of the object are examined, and a formulation based on maximum likelihood estimation is developed. Joint object recognition and modeling are achieved by solving the optimization problem. To evaluate the framework, we have developed a method for simultaneously learning multiple 3-D object models directly from the cluttered indoor environment and tested it using several everyday scenes. Experimental results demonstrate that the framework can cope with the recognition and modeling problem together nicely.