Polyhedral object detection and pose estimation for augmented reality applications

In augmented reality applications, tracking and registration of both cameras and objects is required because, to combine real and rendered scenes, we must project synthetic models at the right location in real images. Although much work has been done to track objects of interest, initialization of theses trackers often remains manual. Our work aims at automating this step by integrating object recognition and tracking into an AR system. Our emphasis is on the initialization phase of the tracking. We address all the three major aspects of the Problem of model-to-image registration: feature detection, correspondence and pose estimation. We have developed a novel approach based on facet detection that greatly reduces the number of possible feature correspondences making it possible to directly compute the transformation which best maps 3-D object to the image plane. We will argue that this approach offers a one-fold speed-up over existing methods. Results of our AR system which integrates initialization and tracking are shown. Our method takes about 5 seconds on our example images.