Automatic Pose Estimation for Range Images on the GPU

Abstract

Object pose (location and orientation) estimation is a common task in many computer vision applications. Although many methods exist, most algorithms need manual initialization and lack robustness to illumination variation, appearance change, and partial occlusions. We propose a fast method for automatic pose estimation without manual initialization based on shape matching of a 3D model to a range image of the scene. We developed a new error function to compare the input range image to pre-computed range maps of the 3D model. We use the tremendous data-parallel processing performance of modern graphics hardware to evaluate and minimize the error function on many range images in parallel. Our algorithm is simple and accurately estimates the pose of partially occluded objects in cluttered scenes in about one second.