Learning Real-Time Perspective Patch Rectification

Abstract

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.The first method favors fast run-time performance while the second one is designed for fast real-time learning and robustness. However, they follow the same general approach: First, a classifier provides for every keypoint a first estimate of its transformation. Then, the estimate allows carrying out an accurate perspective rectification using linear predictors. The last step is a fast verification—made possible by the accurate perspective rectification—of the patch identity and its sub-pixel precision position estimation. We demonstrate the advantages of our approach on real-time 3D object detection and tracking applications.