Pose Estimation using a Hierarchical 3D Representation of Contours and Surfaces

We present a system for detecting the pose of rigid objects using texture and contour information. From a stereo image view of a scene, a sparse hierarchical scene representation is reconstructed using an early cognitive vision system. We define an object model in terms of a simple context descriptor of the contour and texture features to provide a sparse, yet descriptive object representation. Using our descriptors, we do a search in the correspondence space to perform outlier removal and compute the object pose. We perform an extensive evaluation of our approach with stereo images of a variety of real-world objects rendered in a controlled virtual environment. Our experiments show the complementary role of 3D texture and contour information allowing for pose estimation with high robustness and accuracy.