Abstract
Reliable object pose estimation is an integral part of robotic vision systems as it enables robots to manipulate their surroundings. Powerful methods exist that estimate object poses from RGB and RGB-D images, yielding a set of hypotheses per object. However, determining the best hypotheses from the set of possible combinations is a challenging task. We apply MCTS to this problem to find an optimal solution in limited time and propose to share information between equivalent object combinations that emerge during the tree search, so-called transpositions. Thereby, the number of combinations that need to be considered is reduced and the search gathers information on these transpositions in a single statistic. We evaluate the resulting verification method on the YCB-VIDEO dataset and show more reliable detection of the best solution as compared to state of the art. In addition, we report a significant speed-up compared to previous MCTS-based methods for object pose verification.
Funded by the TU Wien Doctoral College TrustRobots. Partially funded by OMRON Corporation and Aeolus, Inc.
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References
Aldoma, A., Tombari, F., Di Stefano, L., Vincze, M.: A global hypotheses verification method for 3D object recognition. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7574, pp. 511–524. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33712-3_37
Aldoma, A., Tombari, F., Di Stefano, L., Vincze, M.: A global hypothesis verification framework for 3D object recognition in clutter. IEEE TPAMI 38(7), 1383–1396 (2016)
Auer, P., Cesa-Bianchi, N., Fischer, P.: Finite-time analysis of the multiarmed bandit problem. Mach. Learn. 47(2–3), 235–256 (2002)
Bauer, D., Patten, T., Vincze, M.: 6D object pose verification via confidence-based Monte Carlo tree search and constrained physics simulation. In: OAGM & ARW Joint Workshop, pp. 153–158 (2019)
Brachmann, E., Michel, F., Krull, A., Yang, M.Y., Gumhold, S., et al.: Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In: IEEE CVPR, pp. 3364–3372 (2016)
Browne, C.B., et al.: A survey of Monte Carlo tree search methods. T-CIAIG 4(1), 1–43 (2012)
Childs, B.E., Brodeur, J.H., Kocsis, L., et al.: Transpositions and move groups in Monte Carlo tree search. In: IEEE CIG, pp. 389–395 (2008)
Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: IEEE CVPR, pp. 998–1005 (2010)
Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303–338 (2010)
Gaudel, R., Sebag, M.: Feature selection as a one-player game. In: ICML, pp. 359–366 (2010)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: IEEE ICCV, pp. 2980–2988 (2017)
Hinterstoisser, S., et al.: Model based training, detection and pose estimation of texture-less 3D objects in heavily cluttered scenes. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 548–562. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37331-2_42
Hodaň, T., et al.: BOP: benchmark for 6D object pose estimation. In: ECCV, pp. 19–34 (2018)
Hodaň, T., Zabulis, X., Lourakis, M., Obdržálek, Š., Matas, J.: Detection and fine 3D pose estimation of texture-less objects in RGB-D images. In: IEEE/RSJ IROS, pp. 4421–4428 (2015)
Kehl, W., Milletari, F., Tombari, F., Ilic, S., Navab, N.: Deep learning of local RGB-D patches for 3D object detection and 6D pose estimation. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 205–220. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_13
Mitash, C., Boularias, A., Bekris, K.E.: Improving 6D pose estimation of objects in clutter via physics-aware Monte Carlo tree search. In: IEEE ICRA, pp. 1–8 (2018)
Narayanan, V., Likhachev, M.: Perch: perception via search for multi-object recognition and localization. In: IEEE ICRA, pp. 5052–5059 (2016)
Pélissier, A., Nakamura, A., Tabata, K.: Feature selection as Monte-Carlo search in growing single rooted directed acyclic graph by best leaf identification. In: SDM, pp. 450–458 (2019)
Saffidine, A., Cazenave, T., Méhat, J.: UCD: upper confidence bound for rooted directed acyclic graphs. Knowl.-Based Syst. 34, 26–33 (2012)
Tejani, A., Tang, D., Kouskouridas, R., Kim, T.-K.: Latent-class hough forests for 3D object detection and pose estimation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8694, pp. 462–477. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10599-4_30
Vidal, J., Lin, C.Y., MartÃ, R.: 6d pose estimation using an improved method based on point pair features. In: ICCAR, pp. 405–409 (2018)
Wang, C., et al.: DenseFusion: 6D object pose estimation by iterative dense fusion. In: IEEE CVPR (2019)
Wohlhart, P., Lepetit, V.: Learning descriptors for object recognition and 3D pose estimation. In: IEEE CVPR, pp. 3109–3118 (2015)
Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: Posecnn: a convolutional neural network for 6D object pose estimation in cluttered scenes. In: CoRR (2017)
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Bauer, D., Patten, T., Vincze, M. (2019). Monte Carlo Tree Search on Directed Acyclic Graphs for Object Pose Verification. 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_35
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