Precise Registration of 3D Models To Images by Swarming Particles

The precise alignment of a 3D model to 2D sensor images to recover the pose of an object in a scene is an important topic in computer vision. In this work, we outline a registration scheme to align arbitrary standard 3D models to optical and synthetic aperture radar (SAR) images in order to recover the full 6 degrees of freedom of the object. We propose a novel similarity measure which combines perspective contour matching and an appearance-based Mutual information (MI) measure. Unlike previous work, the resulting similarity measure is optimized using an evolutionary particle swarming strategy, parallelized to exploit the hardware acceleration potential of current generation graphics processors (GPUs). The performance of our registration scheme is systematically evaluated on an object tracking task using synthetic as well as real input images. We show that our approach leads to precise registration results, even for significant image noise, small object dimensions and partial occlusion where other methods would fail.