Real-Time Learning Of Accurate Patch Rectification

Recent work [5, 6] showed that learning-based patch rectification methods are both faster and more reliable than affine region methods. Unfortunately, their performance improvements are founded in a computationally expensive offline learning stage, which is not possible for applications such as SLAM. In this paper we propose an approach whose training stage is fast enough to be performed at run-time without the loss of accuracy or robustness. To this end, we developed a very fast method to compute the mean appearances of the feature points over sets of small variations that span the range of possible camera viewpoints. Then, by simply matching incoming feature points against these mean appearances, we get a coarse estimate of the viewpoint that is refined afterwards. Because there is no need to compute descriptors for the input image, the method is very fast at run-time. We demonstrate our approach on tracking-by-detection for SLAM, real-time object detection and pose estimation applications.