Skip to Main Content
It is still a difficult problem to establish correspondences of feature points and to estimate view relations for multiple images of a static scene, if the images have large disparities. In this paper we explore the possibility of applying a cheap and general-purpose 3D orientation sensor to improve the robustness of matching such two images. We attach a 3D orientation sensor to a camera and use the system to acquire the images. The camera orientation is obtained from the sensor. Assuming known intrinsic parameters of the camera, we are to estimate only the camera translation between the two views. Owing to the small number of parameters needed to be estimated, it becomes possible to apply a voting method. We show that the method by voting is more robust than the methods based on random sampling, especially for difficult pair of images to make correspondences. In addition, using the known camera orientation, the images can be rectified so that it is as if they were taken by parallel cameras, before the candidate matches are searched for. This helps finding as many correct matches as possible for pairs of images that include rotation around the camera axis. Experimental results for synthetic images as well as real images are shown.