Real-Time Monocular Pose Estimation of 3D Objects using Temporally Consistent Local Color Histograms

We present a novel approach to 6DOF pose estimation and segmentation of rigid 3D objects using a single monocular RGB camera based on temporally consistent, local color histograms. We show that this approach outperforms previous methods in cases of cluttered backgrounds, heterogenous objects, and occlusions. The proposed histograms can be used as statistical object descriptors within a template matching strategy for pose recovery after temporary tracking loss e.g. caused by massive occlusion or if the object leaves the camera's field of view. The descriptors can be trained online within a couple of seconds moving a handheld object in front of a camera. During the training stage, our approach is already capable to recover from accidental tracking loss. We demonstrate the performance of our method in comparison to the state of the art in different challenging experiments including a popular public data set.