3D Distance Metric for Pose Estimation and Object Recognition from 2D Projections

Model based object recognition and model based pose estimation require a distance metric to nd the optimal pose and to measure the distance between the measurements and possible models during the recognition process. When the measurements are given in 2D (such as in orthographic and perspective projections) the commonly used distance between the 3D model features and the 2D image features is the 2D Euclidean distance measured in the image plane. However, this 2D distance does not, usually, increase monotonically with the real 3D distance and thus does not really represent the distance being measured. In this paper we propose a new scheme in which both the optimal positioning and the evaluation of similarity between the 2D image and the model is performed relative to the 3D distance. This distance is calculated between the model features and a 3D predicted object which is a permissible reconstruction of the measured object and is the \closest" to the model features.