Real-time Model-based Rigid Object Pose Estimation and Tracking Combining Dense and Sparse Visual Cues

We propose a novel model-based method for estimating and tracking the six-degrees-of-freedom (6DOF) pose of rigid objects of arbitrary shapes in real-time. By combining dense motion and stereo cues with sparse keypoint correspondences, and by feeding back information from the model to the cue extraction level, the method is both highly accurate and robust to noise and occlusions. A tight integration of the graphical and computational capability of Graphics Processing Units (GPUs) results in pose updates at framerates exceeding 60 Hz. Since a benchmark dataset that enables the evaluation of stereo-vision-based pose estimators in complex scenarios is currently missing in the literature, we have introduced a novel synthetic benchmark dataset with varying objects, background motion, noise and occlusions. Using this dataset and a novel evaluation methodology, we show that the proposed method greatly outperforms state-of-the-art methods. Finally, we demonstrate excellent performance on challenging real-world sequences involving object manipulation.