Prior Knowledge About Camera Motion for Outlier Removal in Feature Matching

The search of corresponding points in between images of the same scene is a well known problem in many computer vision applications. In particular most structure from motion techniques depend heavily on the correct estimation of corresponding image points. Most commonly used approaches make neither assumptions about the 3D scene nor about the relative positions of the cameras and model both as completely unknown. This general model results in a brute force comparison of all keypoints in one image to all points in all other images. In reality this model is often far too general because coarse prior knowledge about the cameras is often available. For example, several imaging systems are equipped with positioning devices which deliver pose information of the camera. Such information can be used to constrain the subsequent point matching not only to reduce the computational load, but also to increase the accuracy of path estimation and 3D reconstruction. This study presents Guided Matching as a new matching algorithm towards this direction. The proposed algorithm outperforms brute force matching in speed as well as number and accuracy of correspondences, given well estimated priors.