Pose Estimation of Known Objects by Efficient Silhouette Matching

Pose estimation is essential for automated handling of objects. In many computer vision applications only the object silhouettes can be acquired reliably, because untextured or slightly transparent objects do not allow for other features. We propose a pose estimation method for known objects, based on hierarchical silhouette matching and unsupervised clustering. The search hierarchy is created by an unsupervised clustering scheme, which makes the method less sensitive to parametrization, and still exploits spatial neighborhood for efficient hierarchy generation. Our evaluation shows a decrease in matching time of 80% compared to an exhaustive matching and scalability to large models.