Skip to main content

Classification of Point Clouds with Neural Networks and Continuum-Type Memories

  • Conference paper
  • First Online:
Artificial Intelligence Applications and Innovations (AIAI 2021)

Abstract

This paper deals with the issue of evaluating and analyzing geometric point sets in three-dimensional space. Point sets or point clouds are often the product of 3D scanners and depth sensors, which are used in the field of autonomous movement for robots and vehicles. Therefore, for the classification of point sets within an active motion, not fully generated point clouds can be used, but knowledge can be extracted from the raw impulses of the respective time points. Attractors consisting of a continuum of stationary states and hysteretic memories can be used to couple multiple inputs over time given non-independent output quantities of a classifier and applied to suitable neural networks. In this paper, we show a way to assign input point clouds to sets of classes using hysteretic memories, which are transferable to neural networks.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    In passive Sonar, the target object itself rather than the sensing device emits a sound signal. This signal can be identified by its characteristic signal profile.

References

  1. Aggarwal, C.C., Reddy, C.K.: Data Clustering - Algorithms and Applications. CRC Press, Boca Raton (2013)

    Book  Google Scholar 

  2. AI and Robotics for GeoEnvironmental Modeling and Monitoring (AIRGEMM). https://tu-freiberg.de/airgemm Accessed 12 Mar 2021

  3. Kisner., H., Thomas., U.: Segmentation of 3d point clouds using a new spectral clustering algorithm without a-priori knowledge. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, vol. 4m VISAPP, pp. 315–322. INSTICC, SciTePress (2018). https://doi.org/10.5220/0006549303150322

  4. Nakagawa, M.: Point cloud clustering using panoramic layered range image. IntechOpen (2018). https://doi.org/10.5772/intechopen.76407

    Article  Google Scholar 

  5. Carrillo, J., Perthame, B., Salort, D., Smets, D.: Qualitative properties of solutions for the noisy integrate and fire model in computational neuroscience. Comput. Neurosci. Nonlinearity 28(9), 3365–3388 (2015)

    MathSciNet  MATH  Google Scholar 

  6. Comsa, I. M., Potempa, K., Versari, L., Fischbacher, T., Gesmundo, A., Alakuijala, J.: Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function: Learning with Backpropagation (2020). https://arxiv.org/pdf/1907.13223.pdf

  7. Eliasmith, C.: A unified approach to building and controlling spiking attractor networks. Neural Comput. 17(6), 1276–1314 (2005)

    Article  MathSciNet  Google Scholar 

  8. Farrokh, M., Dizaji, M., Dizaji, F., Moradinasab, N.: Universal hysteresis identification using extended Preisach Neural Network (2019). https://arxiv.org/pdf/2001.01559.pdf

  9. Fusi, S.: Hebbian spike-driven synaptic plasticity for learning patterns of mean firing rates. Biol. Cybern. 87(5–6), 459–470 (2002)

    Article  Google Scholar 

  10. Kuznetsov, N., Reitmann, V.: Attractor Dimension Estimates for Dynamical Systems: Theory and Computation. ECC, vol. 38. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-50987-3

    Book  Google Scholar 

  11. Mutto, C.D., Zanuttigh, P., Cortelazzo, G.M.: Time-of-Flight Cameras and Microsoft Kinectâ„¢; Springer, Boston (2012) https://doi.org/10.1007/978-1-4614-3807-6_2

  12. Neimark, Yu. I.: On Lyapunov stability of systems with distributed wave units. Uchenye Zapiski Gor’kovskogo Gos. Universiteta, Ser. Fiz., XVI (1950) (Russian)

    Google Scholar 

  13. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: Deep learning on point sets for 3d classification and segmentation. abs/1612.00593 (2016). http://arxiv.org/abs/1612.00593

  14. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. abs/1706.02413 (2017). http://arxiv.org/abs/1706.02413

  15. Rajpura, P.S., Goyal, M., Bojinov, H., Hegde, R.S.: Dataset augmentation with synthetic images improves semantic segmentation. CoRR abs/1709.00849 (2017). http://arxiv.org/abs/1709.00849

  16. Reitmann, S., Neumann, L., Jung, B.: BLAINDER—a blender AI add-on for generation of semantically labeled depth-sensing data. Sensors 21, 2144 (2021). https://doi.org/10.3390/s21062144

  17. Reitmann, V.: Convergence in evolutionary variational inequalities with hysteresis nonlinearities. In: Proceedings of Equadiff 11, Bratislava, pp. 395–404 (2005)

    Google Scholar 

  18. Reitmann, V.: Realization theory methods for the stability investigation of nonlinear infinite-dimensional input-output systems. Mathematica BOHEMICA 136(2), 185–194 (2011)

    Article  MathSciNet  Google Scholar 

  19. Reitmann, V., Zyryanov, D.: The global attractor of a multivalued dynamical system generated by a two-phase heating problem. Differ. Eqn. Control Processes 4, 118–138 (2017). (Russian)

    MATH  Google Scholar 

  20. Seeholzer, A., Deger, M., Gerstner, W.: Stability of working memory in continuous attractor networks under the control of short-term plasticity. PLoS Comput. Biol. 15(4), e1006928 (2019)

    Google Scholar 

  21. Smirnova, V.B.: On the asymptotic behavior of a class of control systems with distributed parameters. Avtomatika i Telemekhanika 10, 5–12 (1973). (Russian)

    Google Scholar 

  22. Visintin, A.: Differential Models of Hysteresis. Springer, Berlin (1994). https://doi.org/10.1007/978-3-662-11557-2

    Book  MATH  Google Scholar 

  23. Jyh-Da, W., Chuen-Tsai, S.: Constructing hysteretic memory in neural networks. IEEE Trans. Syst. Man Cybern. Part B 30(4), 601–609 (2000) Journal 2(5), 99–110 (2016)

    Google Scholar 

  24. Yi, L., et al.: A scalable active framework for region annotation in 3d shape collections. ACM Trans. Graph. 35(6) (2016). https://doi.org/10.1145/2980179.2980238

Download references

Acknowledgement

Part 3 and 4 of the work are supported by the 2020–2021 program Leading Scientific Schools of the Russian Federation (project NSh-2624.2020.1) and Saint Petersburg State University (ID 75206671).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefan Reitmann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Reitmann, S., Kudryashova, E.V., Jung, B., Reitmann, V. (2021). Classification of Point Clouds with Neural Networks and Continuum-Type Memories. In: Maglogiannis, I., Macintyre, J., Iliadis, L. (eds) Artificial Intelligence Applications and Innovations. AIAI 2021. IFIP Advances in Information and Communication Technology, vol 627. Springer, Cham. https://doi.org/10.1007/978-3-030-79150-6_40

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79150-6_40

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79149-0

  • Online ISBN: 978-3-030-79150-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics