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Using Neural Factorization of Shape and Reflectance for Ball Detection

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RoboCup 2023: Robot World Cup XXVI (RoboCup 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14140))

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Abstract

Visually detecting a well-defined object like a soccer ball should be a simple problem. Under good lighting conditions this problem can be claimed to be solved, but unfortunately, the lighting conditions are not always optimal. In those circumstances, it is valuable to have a shape and reflectance model of the ball, to be able to predict how its appearance changes if the lighting changes. This is a prerequisite for playing soccer outside, with direct sunlight and clouds. The predicted appearance will be used to fine-tune an existing ball detection algorithm, based on the classic Yolo algorithm.

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Notes

  1. 1.

    https://www.intelligentroboticslab.nl/.

  2. 2.

    https://spl.robocup.org/.

  3. 3.

    https://github.com/AlexeyAB, maintained by Alexey Bochkovsky.

References

  1. Bolt, L., Klein Gunnewiek, F., Lekanne gezegd Deprez, H., van Iterson, L., Prinzhorn, D.: Dutch Nao Team - Technical report, December 2022

    Google Scholar 

  2. Borkman, S., et al.: Unity perception: generate synthetic data for computer vision, July 2021. arXiv preprint 2107.04259

    Google Scholar 

  3. Chown, E., Lagoudakis, M.G.: The standard platform league. In: Bianchi, R., Akin, H., Ramamoorthy, S., Sugiura, K. (eds.) RoboCup 2014: Robot World Cup XVIII. RoboCup 2014. LNCS, vol. 8992, pp. 636–648. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-18615-3_52

  4. Fiedler, N., Bestmann, M., Hendrich, N.: Imagetagger: an open source online platform for collaborative image labeling. In: Holz, D., Genter, K., Saad, M., von Stryk, O. (eds.) RoboCup 2018: Robot World Cup XXII. RoboCup 2018. LNCS, vol. 11374, pp. 162–169. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-27544-0_13

  5. Hayes-Roth, B.: Architectural foundations for real-time performance in intelligent agents. Real-Time Syst. 2(1–2), 99–125 (1990)

    Article  Google Scholar 

  6. Hess, T., Mundt, M., Weis, T., Ramesh, V.: Large-scale stochastic scene generation and semantic annotation for deep convolutional neural network training in the RoboCup SPL. In: Akiyama, H., Obst, O., Sammut, C., Tonidandel, F. (eds.) RoboCup 2017: Robot World Cup XXI. RoboCup 2017. LNCS, vol. 11175, pp. 33–44. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00308-1_3

  7. Jiao, L., et al.: A survey of deep learning-based object detection. IEEE Access 7, 128837–128868 (2019)

    Article  Google Scholar 

  8. Kahlefendt, C.: A Comparison and Evaluation of Neural Network-based Classification Approaches for the Purpose of a Robot Detection on the Nao Robotic System. Master’s thesis, Technische Universität Hamburg-Harburg, April 2017

    Google Scholar 

  9. Li, C., et al.: Yolov6 v3.0: a full-scale reloading, January 2023. arXiv 2301.05586

    Google Scholar 

  10. Li, G., Song, Z., Fu, Q.: A new method of image detection for small datasets under the framework of yolo network. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), pp. 1031–1035, October 2018

    Google Scholar 

  11. Lin, T.Y., et al.: Microsoft coco: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) Computer Vision – ECCV 2014. ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48

  12. Martin-Brualla, R., Radwan, N., Sajjadi, M.S.M., Barron, J.T., Dosovitskiy, A., Duckworth, D.: Nerf in the wild: neural radiance fields for unconstrained photo collections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7210–7219, June 2021

    Google Scholar 

  13. Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: representing scenes as neural radiance fields for view synthesis. Commun. ACM 65(1), 99–106 (2021)

    Article  Google Scholar 

  14. Monté, X.: Neural factorization of shape and reflectance of a football under an unknown illumination. Bachelor thesis, University of Amsterdam, February 2023

    Google Scholar 

  15. Mungan, C.: Bidirectional reflectance distribution functions describing first-surface scattering. AFOSR Final Report for the Summer Faculty Research Program (Summer 1998)

    Google Scholar 

  16. Narayanaswami, S.K., et al.: Towards a real-time, low-resource, end-to-end object detection pipeline for robot soccer. In: Eguchi, A., Lau, N., Paetzel-Prusmann, M., Wanichanon, T. (eds.) RoboCup 2022:. RoboCup 2022. LNCS, vol. 13561, pp. 62–74. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-28469-4_6

  17. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, January 2016

    Google Scholar 

  18. Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7263–7271, July 2017

    Google Scholar 

  19. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement, April 2018. arXiv 1804.02767

    Google Scholar 

  20. Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4104–4113, June 2016

    Google Scholar 

  21. Specchi, G., et al.: Structural pruning for real-time multi-object detection on NAO robots. In: RoboCup 2023: Robot World Cup XXVI, July 2023

    Google Scholar 

  22. Tang, J., et al.: Delicate textured mesh recovery from nerf via adaptive surface refinement, March 2023. arXiv preprint 2303.02091

    Google Scholar 

  23. Terven, J., Cordova-Esparza, D.: A comprehensive review of yolo: from yolov1 to yolov8 and beyond, April 2023. arXiv 2304.00501

    Google Scholar 

  24. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Scaled-yolov4: scaling cross stage partial network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13029–13038, June 2021

    Google Scholar 

  25. Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Yolov7: trainable bag-of-freebies sets new state-of-the-art for real-time object detectors, July 2022. arXiv 2207.02696

    Google Scholar 

  26. van der Weerd, R.: Real-time object detection and avoidance for autonomous NAO robots performing in the standard platform league. Project report, University of Amsterdam, July 2021

    Google Scholar 

  27. Zaidi, S.S.A., Ansari, M.S., Aslam, A., Kanwal, N., Asghar, M., Lee, B.: A survey of modern deep learning based object detection models. Digit. Signal Process. 126, 103514 (2022)

    Google Scholar 

  28. Zhang, X., Srinivasan, P.P., Deng, B., Debevec, P., Freeman, W.T., Barron, J.T.: Nerfactor: neural factorization of shape and reflectance under an unknown illumination. ACM Trans. Graph. 40(6) (2021)

    Google Scholar 

  29. Zou, Z., Chen, K., Shi, Z., Guo, Y., Ye, J.: Object detection in 20 years: a survey. Proc. IEEE 111(3), 257–276 (2023)

    Article  Google Scholar 

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Authors and Affiliations

  1. Intelligent Robotics Lab, Universiteit van Amsterdam, Amsterdam, The Netherlands

    Xavier Monté, Joey van der Kaaij, Rogier van der Weerd & Arnoud Visser

Authors
  1. Xavier Monté
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  2. Joey van der Kaaij
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  3. Rogier van der Weerd
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  4. Arnoud Visser
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Corresponding author

Correspondence to Arnoud Visser .

Editor information

Editors and Affiliations

  1. ENIB/Naval Group Pacific, Plouzané, France

    Cédric Buche

  2. University of Naples Federico II, Naples, Italy

    Alessandra Rossi

  3. Bahia State University, Salvador, Brazil

    Marco Simões

  4. University of Miami, Coral Gables, FL, USA

    Ubbo Visser

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Monté, X., van der Kaaij, J., van der Weerd, R., Visser, A. (2024). Using Neural Factorization of Shape and Reflectance for Ball Detection. In: Buche, C., Rossi, A., Simões, M., Visser, U. (eds) RoboCup 2023: Robot World Cup XXVI. RoboCup 2023. Lecture Notes in Computer Science(), vol 14140. Springer, Cham. https://doi.org/10.1007/978-3-031-55015-7_11

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  • DOI: https://doi.org/10.1007/978-3-031-55015-7_11

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  • Online ISBN: 978-3-031-55015-7

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