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International Conference on Artificial Intelligence and Soft Computing

ICAISC 2021: Artificial Intelligence and Soft Computing pp 36–43Cite as

An Efficient Technique for Filtering of 3D Cluttered Surfaces

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12855))

Abstract

3D Object recognition is a rapidly growing research field in the area of computer vision. The presence of cluttered surfaces is the main obstacle to object recognition. In this paper, we propose to present a technique for automatic filtering of cluttered surfaces. First, the technique clusters a point cloud and then based on three features that are size, distance and spatial information, cluttered surfaces are separated. To the best of our knowledge, this is the first technique that can remove any cluttered surface (plane or irregular) from a point cloud. We have experimented on two complex RGBD datasets containing heavily cluttered surfaces and using the proposed metric, we measure the remaining cluttered surfaces after filtering of a point cloud. The proposed clutter filtering has removed 87.60% and 89.68% cluttered surfaces for Challenge and Willow datasets respectively.

This research was conducted as part of the project called “Reuse and Recycling of Lithium-Ion Batteries” (RELIB). This work was supported by the Faraday Institution [grant number FIRG005].

P. Joshi and A. Rastegarpanah are identified as joint lead authors of this work

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References

  1. Aldoma, A., Fäulhammer, T., Vincze, M.: Automation of ground truth annotation for multi-view RGB-D object instance recognition datasets. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 5016–5023 (2014)

    Google Scholar 

  2. Aldoma, A., Tombari, F., Di Stefano, L., Vincze, M.: A global hypotheses verification method for 3D object recognition. In: Proceedings of European Conference on Computer Vision, pp. 511–524 (2012)

    Google Scholar 

  3. Ballard, D.: Generalizing the hough transform to detect arbitrary shapes. Patt. Recogn. 13(2), 111–122 (1981)

    Article  Google Scholar 

  4. Borrmann, D., Elseberg, J., Lingemann, K., Nüchter, A.: The 3D hough transform for plane detection in point clouds: a review and a new accumulator design. 3D Research 2, 1–13 (2011)

    Google Scholar 

  5. Czerniawski, T., Nahangi, M., Walbridge, S., Haas, C.: Automated removal of planar clutter from 3D point clouds for improving industrial object recognition. In: Proceedings of the 33rd International Symposium on Automation and Robotics in Construction (ISARC), pp. 357–365 (2016)

    Google Scholar 

  6. 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 

  7. Joshi, P., Rastegarpanah, A., Stolkin, R.: Are current 3D descriptors ready for real-time object recognition? In: Proceedings of 8th IEEE International Conference on Control, Mechatronics and Automation (ICCMA 2020), pp. 217–221 (2020)

    Google Scholar 

  8. Joshi, P., Rastegarpanah, A., Stolkin, R.: A survey on training free 3D texture-less object recognition techniques. In: Proceedings of IEEE International Conference on Digital Image Computing: Techniques and Applications (DICTA), pp. 1–3 (2020)

    Google Scholar 

  9. Joshi, P., Rastegarpanah, A., Stolkin, R.: A training free technique for 3D object recognition using the concept of vibration, energy and frequency. Comput. Graph. 95, 92–105 (2021)

    Article  Google Scholar 

  10. Kaiser, M., Xu, X., Kwolek, B., Sural, S., Rigoll, G.: Towards using covariance matrix pyramids as salient point descriptors in 3D point clouds. Neurocomputing 120, 101–112 (2013)

    Article  Google Scholar 

  11. Rabbani, T., Van Den Heuvel, F., Vosselmann, G.: Segmentation of point clouds using smoothness constraint. Int. Arch. Photogrammetry, Remote Sens. spat. Inf. Sci. 36(5), 248–253 (2006)

    Google Scholar 

  12. Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: Proceedings of IEEE International Conference on Robotics and Automation, pp. 1–4 (2011)

    Google Scholar 

  13. Schnabel, R., Wahl, R., Klein, R.: Efficient ransac for point-cloud shape detection. Comput. Graph. Forum 26(2), 214–226 (2007)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The Faraday Institution, Quad One, Harwell Science and Innovation Campus, Didcot, UK

    Piyush Joshi, Alireza Rastegarpanah & Rustam Stolkin

  2. School of Metallurgy and Materials, University of Birmingham, Birmingham, B15 2TT, UK

    Piyush Joshi, Alireza Rastegarpanah & Rustam Stolkin

Authors
  1. Piyush Joshi
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  2. Alireza Rastegarpanah
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  3. Rustam Stolkin
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Editor information

Editors and Affiliations

  1. Czestochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Krakow, Poland

    Ryszard Tadeusiewicz

  6. Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Joshi, P., Rastegarpanah, A., Stolkin, R. (2021). An Efficient Technique for Filtering of 3D Cluttered Surfaces. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2021. Lecture Notes in Computer Science(), vol 12855. Springer, Cham. https://doi.org/10.1007/978-3-030-87897-9_4

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  • DOI: https://doi.org/10.1007/978-3-030-87897-9_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87896-2

  • Online ISBN: 978-3-030-87897-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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