Constraint-Fusion for Interpretation of Articulated Objects

Abstract

This paper presents a method for interpretation of modeled objects that is general enough to cover articulated and other types of constrained models. The flexibility between components of the model are expressed as spatial constraints which are fused into the pose estimation during the interpretation process. The constraint fusion assists in obtaining the correct interpretation and in reducing the search of possible correspondences. The proposed method can handle any constraint (including inequalities) between any number of different components of the model. The framework is based on Kalman filtering. 1 Introduction Estimating the pose of a 3D object from images or other sensed data is a classical problem in computer vision. Quite often, a model of the object is known and this information is used to estimate the pose of the object in the world. This problem is known as modelbased pose determination and is used in many applications such as object recognition, object tracking, robot nav...