Abstract
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.
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The work presented in this paper has been partially supported by Aeolus Robotics, Inc.
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Alexandrov, S.V., Patten, T., Vincze, M. (2019). Leveraging Symmetries to Improve Object Detection and Pose Estimation from Range Data. In: Tzovaras, D., Giakoumis, D., Vincze, M., Argyros, A. (eds) Computer Vision Systems. ICVS 2019. Lecture Notes in Computer Science(), vol 11754. Springer, Cham. https://doi.org/10.1007/978-3-030-34995-0_36
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DOI: https://doi.org/10.1007/978-3-030-34995-0_36
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