Learning Real-Time Perspective Patch Rectification

We propose two learning-based methods to patch rectification that are faster and more reliable than state-of-the-art affine region detection methods. Given a reference view of a patch, they can quickly recognize it in new views and accurately estimate the homography between the reference view and the new view. Our methods are more memory-consuming than affine region detectors, and are in practice currently limited to a few tens of patches. However, if the reference image is a fronto-parallel view and the internal parameters known, one single patch is often enough to precisely estimate an object pose. As a result, we can deal in real-time with objects that are significantly less textured than the ones required by state-of-the-art methods.