Exploiting spatial consistency for object classification and pose estimation

In this paper we present a novel object classification and pose recovery algorithm which takes advantage of existing 3D models and multiple synchronized and calibrated views. Having a calibrated scenario provides redundant data which can be exploited for gathering spatial consistency of an object´s 3D pose and its class. In a first step, the cameras need to be calibrated and aligned to one common coordinate system. A training set of 3D models, a calibrated setup and Harris corner features are used to find the best fitting 2D projection for an object within the scene. The results are improved by aligning multiple synchronized views to gain spatial consistency. Our experiments using real data show the enhanced results using a calibrated setup over analyzing each camera separately.