Recognizing 3d Objects from 2d Images: an Error Analysis Recognizing 3d Objects from 2d Images: an Error Analysis

Many recent object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. In the case of perfect image data, these systems seem to work well. With uncertain image data, however, the performance of such methods is less well understood. In this paper, we examine the eeects of two-dimensional sensor uncertainty on the computation of three-dimensional model transformations. We use this analysis to bound the uncertainty in the transformation parameters, as well as the uncertainty associated with applying the transformation to map other model features into the image. We also examine the eeects of the transformation uncertainty on the eeectiveness of recognition methods. Abstract. Many recent object recognition systems use a small number of pairings of data and model features to compute the 3D transformation from a model coordinate frame into the sensor coordinate system. In the case of perfect image data, these systems seem to work well. With uncertain image data, however, the performance of such methods is less well understood. In this paper, we examine the eeects of two-dimensional sensor uncertainty on the computation of three-dimensional model transformations. We use this analysis to bound the uncertainty in the transformation parameters, as well as the uncertainty associated with applying the transformation to map other model features into the image. We also examine the eeects of the transformation uncertainty on the eeectiveness of recognition methods.