We present and contrast four methods for registering 3D range data to 2D images. Two are calibration techniques that recover the rigid transformation between the sensor poses, based on point or line correspondences. The two others recover a direct, image based mapping between the data sets. The accuracy of each method is experimentally evaluated on test patterns and objects. We found that the point based calibration method is the best approach to recover a global registration between the two sensors, while an image-based method performed best when registering local regions.