Skip to main content
SpringerLink
Log in

Automatic Viewpoint Estimation for Inspection Planning Purposes

  • Conference paper
  • First Online:
Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12664))

Included in the following conference series:

  • 2215 Accesses

Abstract

Viewpoint estimation is an important aspect of surface inspection and planning. Typically viewpoint estimation has been done only with the 3D model and not with the actual object. This, therefore, limits the flexibility of using the actual object in inspection planning. In this work, we present a novel pipeline that can efficiently estimate viewpoints from live camera images. The pipeline can be used for different sized industrial objects to efficiently estimate the viewpoint. The achieved results are real-time and the method is easily generalizable to different objects. The presented method is based on a 3D model of the object, which is self-supervised and requires no manual data annotation or real images to be used as an input. The complete solution, together with documentation and examples is available in the public domain for testing. https://gitlab.itwm.fraunhofer.de/dutta/real-time-pose-est/.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight 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

References

  1. Buitinck, L., et al.: API design for machine learning software: experiences from the scikit-learn project. In: ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pp. 108–122 (2013)

    Google Scholar 

  2. Chaurasia, A., Culurciello, E.: Linknet: exploiting encoder representations for efficient semantic segmentation (2017)

    Google Scholar 

  3. Everingham, M., Winn, J.: The pascal visual object classes challenge 2012 (voc2012) development kit. Pattern Analysis, Statistical Modelling and Computational Learning, Technical Report, vol. 8 (2011)

    Google Scholar 

  4. Gospodnetic, P., Mosbach, D., Rauhut, M., Hagen, H.: Flexible surface inspection planning pipeline. In: 2020 6th International Conference on Control, Automation and Robotics (ICCAR), pp. 644–652 (2020)

    Google Scholar 

  5. Gospodnetic, P., Rauhut, M., Hagen, H.: Surface inspection planning using 3D visualization. In: LEVIA 2019: Leipzig Symposium on Visualization in Applications (2019)

    Google Scholar 

  6. Gronle, M., Osten, W.: View and sensor planning for multi-sensor surface inspection. Surf. Topogr. Metrol. Prop. 4(2), 024009 (2016)

    Google Scholar 

  7. Hinterstoisser, S., et al.: Gradient response maps for real-time detection of textureless objects. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 876–888 (2011)

    Article  Google Scholar 

  8. Hodaň, T., Haluza, P., Obdržálek, Š., Matas, J., Lourakis, M., Zabulis, X.: T-less: an RGB-D dataset for 6D pose estimation of texture-less objects. In: IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 880–888 (2017)

    Google Scholar 

  9. Kehl, W., Manhardt, F., Tombari, F., Ilic, S., Navab, N.: SSD-6D: making RGB-based 3D detection and 6D pose estimation great again. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1521–1529 (2017)

    Google Scholar 

  10. 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

    Chapter  Google Scholar 

  11. Kendall, A., Grimes, M., Cipolla, R.: Posenet: a convolutional network for real-time 6-DoF camera relocalization. In: IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  12. Kirillov, A., Girshick, R., He, K., Dollár, P.: Panoptic feature pyramid networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6399–6408 (2019)

    Google Scholar 

  13. Lin, T., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: IEEE International Conference on Computer Vision (ICCV), pp. 2980–2988 (2017)

    Google Scholar 

  14. Loh, W.Y.: Classification and regression trees. Wiley Interdisc. Rev. Data Min. Knowl. Discovery 1(1), 14–23 (2011)

    Article  Google Scholar 

  15. Mohammadikaji, M., Bergmann, S., Irgenfried, S., Beyerer, J., Dachsbacher, C., Worn, H.: Inspection planning for optimized coverage of geometrically complex surfaces. In: 2018 Workshop on Metrology for Industry 4.0 and IoT, pp. 52–67 (2018)

    Google Scholar 

  16. Pagani, A.: Modeling reality for camera registration in augmented reality applications. KI-Künstliche Intelligenz 4(28), 321–324 (2014)

    Article  Google Scholar 

  17. 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. 3828–3836 (2017)

    Google Scholar 

  18. Rambach, J., Pagani, A., Schneider, M., Artemenko, O., Stricker, D.: 6DoF object tracking based on 3D scans for augmented reality remote live support. Computers 7(1), 6 (2018)

    Article  Google Scholar 

  19. Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  20. 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 

  21. Sundermeyer, M., Marton, Z., Durner, M., Brucker, M., Triebel, R.: Implicit 3D orientation learning for 6D object detection from RGB images. In: European Conference on Computer Vision (ECCV) (2018)

    Google Scholar 

  22. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)

    Google Scholar 

  23. Tekin, B., Sinha, S.N., Fua, P.: Real-time seamless single shot 6D object pose prediction. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 292–301 (2018)

    Google Scholar 

  24. Wang, C., et al.: Densefusion: 6D object pose estimation by iterative dense fusion. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3343–3352 (2019)

    Google Scholar 

  25. 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 (RSS) (2018)

    Google Scholar 

Download references

Acknowledgement

The authors would like to thank Dr. Thomas Weibel for his valuable inputs and the reviewers for their useful comments. We would also like too thank the Department of Image Processing, Fraunhofer ITWM for funding this project.

Author information

Authors and Affiliations

  1. Department of Computer Science, Technische Universität Kaiserslautern, 67663, Kasierslautern, Germany

    Siddhartha Dutta, Hans Hagen & Petra Gospodnetić

  2. Department of Image Processing, Fraunhofer ITWM, 67663, Kaiserslautern, Germany

    Siddhartha Dutta, Markus Rauhut & Petra Gospodnetić

Authors
  1. Siddhartha Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Markus Rauhut
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hans Hagen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Petra Gospodnetić
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Siddhartha Dutta .

Editor information

Editors and Affiliations

  1. Dipartimento di Ingegneria dell'Informazione, University of Firenze, Florence, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dutta, S., Rauhut, M., Hagen, H., Gospodnetić, P. (2021). Automatic Viewpoint Estimation for Inspection Planning Purposes. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12664. Springer, Cham. https://doi.org/10.1007/978-3-030-68799-1_49

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-68799-1_49

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68798-4

  • Online ISBN: 978-3-030-68799-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation