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3D Model-Based 6D Object Pose Tracking on RGB Images

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Intelligent Information and Database Systems (ACIIDS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12033))

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Abstract

In this paper, we present a 3D-model based algorithm for 6D object pose estimation and tracking on segmented RGB images. The object of interest is segmented by U-Net neural network trained on a set of manually delineated images. A Particle Swarm Optimization is used to estimate the 6D object pose by projecting the 3D object model and then matching the projected image with the image acquired by the camera. The tracking of 6D object pose is formulated as a dynamic optimization problem. In order to keep necessary human intervention minimal, we use an automated turntable setup to prepare a 3D object model and to determine the ground-truth poses. We compare the experimental results obtained by our algorithm with results achieved by PWP3D algorithm.

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References

  1. Brachmann, E., Krull, A., Michel, F., Gumhold, S., Shotton, J., Rother, C.: Learning 6D object pose estimation using 3D object coordinates. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 536–551. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_35

    Chapter  Google Scholar 

  2. 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

    Chapter  Google Scholar 

  3. Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Vis. Comput. Graph. 22(12), 2633–2651 (2016)

    Article  Google Scholar 

  4. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  5. Hinterstoisser, S., et al.: Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes. In: International Conference on Computer Vision, pp. 858–865 (2011)

    Google Scholar 

  6. Model-Based Training, Detection and Pose Estimation of Texture-less 3D Objects in Heavily Cluttered Scenes. In: Proceedings of the Asian Conference on Computer Vision (ACCV) (2012)

    Google Scholar 

  7. Brachmann, E., Michel, F., Krull, A., Yang, M., Gumhold, S., Rother, C.: Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3364–3372 (2016)

    Google Scholar 

  8. Pouyanfar, S., et al.: A survey on deep learning: algorithms, techniques, and applications. ACM Comput. Surv. 51(5), 1–36 (2018)

    Article  Google Scholar 

  9. Wohlhart, P., Lepetit, V.: Learning descriptors for object recognition and 3D pose estimation. In: Conference on Computer Vision and Pattern Recognition, pp. 1–10 (2015)

    Google Scholar 

  10. Xiang, Y., Schmidt, T., Narayanan, V., Fox, D.: PoseCNN: a convolutional neural network for 6D object pose estimation in cluttered scenes. In: Robotics: Science and Systems XIV (RSS) (2018)

    Google Scholar 

  11. Rad, M., Lepetit, V.: BB8: a scalable, accurate, robust to partial occlusion method for predicting the 3D poses of challenging objects without using depth. In: IEEE International Conference on Computer Vision, ICCV, pp. 3848–3856 (2017)

    Google Scholar 

  12. Deng, X., Mousavian, A., Xiang, Y., Xia, F., Bretl, T., Fox, D.: PoseRBPF: a rao-blackwellized particle filter for 6D object pose tracking. In: Robotics: Science and Systems (RSS) (2019)

    Google Scholar 

  13. Gritsenko, P., Gritsenko, I., Seidakhmet, A., Kwolek, B.: Plane-based humanoid robot navigation and object model construction for grasping. In: Leal-Taixé, L., Roth, S. (eds.) ECCV 2018. LNCS, vol. 11129, pp. 649–664. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11009-3_40

    Chapter  Google Scholar 

  14. Kennedy, J., Eberhart, R.: Particle Swarm Optimization. In: Proceedings of IEEE International Conference on Neural Networks, pp. 1942–1948. IEEE Press, Piscataway (1995)

    Google Scholar 

  15. Sengupta, S., Basak, S., Peters, R.A.: Particle swarm optimization: a survey of historical and recent developments with hybridization perspectives. Mach. Learn. Knowl. Extr. 1(1), 157–191 (2019)

    Article  Google Scholar 

  16. Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28

    Chapter  Google Scholar 

  17. Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: ICML, vol. 37, pp. 448–456 (2015)

    Google Scholar 

  18. Ugolotti, R., Nashed, Y.S.G., Mesejo, P., Ivekovič, V., Mussi, L., Cagnoni, S.: Particle swarm optimization and differential evolution for model-based object detection. Appl. Soft Comput. 13(6), 3092–3105 (2013)

    Article  Google Scholar 

  19. Zabulis, X., Lourakis, M.I.A., Stefanou, S.S.: 3D pose refinement using rendering and texture-based matching. In: Chmielewski, L.J., Kozera, R., Shin, B.-S., Wojciechowski, K. (eds.) ICCVG 2014. LNCS, vol. 8671, pp. 672–679. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11331-9_80

    Chapter  Google Scholar 

  20. Rymut, B., Kwolek, B., Krzeszowski, T.: GPU-accelerated human motion tracking using particle filter combined with PSO. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 426–437. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02895-8_38

    Chapter  Google Scholar 

  21. Tan, Y.: GPU-based parallel implementation of swarm intelligence algorithms. Morgan Kaufmann, San Francisco (2016)

    Google Scholar 

  22. Izadi, S., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology, New York, NY, USA, pp. 559–568. ACM (2011)

    Google Scholar 

  23. Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision in C++ with the OpenCV Library, 2nd edn. O’Reilly Media Inc, Sebastopol (2013)

    Google Scholar 

  24. Prisacariu, V.A., Reid, I.D.: PWP3D: real-time segmentation and tracking of 3D objects. Int. J. Comput. Vision 98(3), 335–354 (2012)

    Article  MathSciNet  Google Scholar 

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Acknowledgment

This work was supported by Polish National Science Center (NCN) under a research grant 2017/27/B/ST6/01743.

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Authors and Affiliations

  1. AGH University of Science and Technology, 30 Mickiewicza, 30-059, Krakow, Poland

    Mateusz Majcher & Bogdan Kwolek

Authors
  1. Mateusz Majcher
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  2. Bogdan Kwolek
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Corresponding author

Correspondence to Bogdan Kwolek .

Editor information

Editors and Affiliations

  1. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Kietikul Jearanaitanakij

  3. Faculty of Computer Science and Information, University Teknologi Malaysia, Kuala Lumpur, Malaysia

    Ali Selamat

  4. Department of Applied Informatics, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

    Suphamit Chittayasothorn

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Majcher, M., Kwolek, B. (2020). 3D Model-Based 6D Object Pose Tracking on RGB Images. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12033. Springer, Cham. https://doi.org/10.1007/978-3-030-41964-6_24

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  • DOI: https://doi.org/10.1007/978-3-030-41964-6_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41963-9

  • Online ISBN: 978-3-030-41964-6

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