Image Inpainting using Two -View Epipolar Geometry

We take a fresh look at the problem of removing occluders in an image using inpainting. We examine a geometric method that utilizes a second image of the scene from a different viewpoint, to identify the occluded objects. We recover the missing intensities by using the geometric relationship between corresponding points in the two images. The relationship is generally specified by the “epipolar line constraint”, and degenerates to a projective homography under special circumstances. For this reason, we use the plane + parallax approach to estimate the epipolar geometry relating the two images. We fill-in missing pixels by copying information from the respective epipolar lines in the second image. The success of the proposed framework hinges on the ability to estimate the “epipolar line constraint” from noisy correspondences. To this end, we analyze the uncertainty in estimating a homography from noisy correspondences. We rely on the knowledge of this uncertainty to identify parallax vectors best suited for estimating the epipolar geometry. We do not make any explicit assumptions about the nature or the extent of camera motion, only requiring that the occluded objects are static and undergo limited perspective change. To illustrate the effectiveness of the proposed approach, we “inpaint” occluders in wide-baseline situations.