Abstract
This paper describes results of using different features for the pose refinement of 3D objects i.e. find the best transformation between a 3D model and a 2D view of the object. This is a six degrees of freedom problem, which is seen as essential to solve for 3D object recognition. Previous research has investigated various matching measure for pose refinement using edge points from the model and all edge pixels in the image, in the presence of noise and other objects. This paper extends the work to consider other features such as vertices and lines giving results to show the usefulness of each feature.