Leveraging Symmetries to Improve Object Detection and Pose Estimation from Range Data

Many man-made objects around us exhibit rotational symmetries. This fact can be exploited to improve object detection and 6D pose estimation performance. To this end we propose a set of extensions to the state-of-the-art PPF pipeline. We describe how a fundamental region is selected on symmetrical objects and used to construct a compact model hash table and a Hough voting space without redundancies. We also introduce a symmetry-aware distance metric for the pose clustering step. Our experiments on T-LESS and ToyotaLight datasets demonstrate that these extensions lead to a consistent improvement in the pose estimation recall score compared to the baseline pipeline, while simultaneously reducing computation time by up to 4 times.