Disparity estimation of misaligned images in a scanline optimization framework

Modern, state-of-the-art disparity estimation techniques are able to very accurately estimate the disparity for a wide variety of scene types. However all of these methods assume that the input images are epipolar rectified. When an image pair is not rectified, it must be pre-processed before any estimation can be done. In this paper we propose a disparity estimation scheme that is able to handle non-rectified images without requiring a rectification step. We show how a minor modification to an existing estimation framework can allow for any disparity estimation framework to produce disparity maps for non-rectified images.