Skip to main content
SpringerLink
Log in

Point Pair Features Based Object Recognition with Improved Training Pipeline

  • Conference paper
  • First Online:
Intelligent Robotics and Applications (ICIRA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10985))

Included in the following conference series:

  • 2876 Accesses

Abstract

PPF (point pair feature) is a widely used framework in object detection and pose estimation. However, it is computational expensive and sensitive to cluster and occlusions. In this paper, we propose a new training pipeline for PPF which makes use of the visibility information of point pairs, yet with no extra computation cost. We also design a strategy to employ plane features to make PPF more discriminative and efficient. Our experiment results show that our method achieves competitive results compared with some state-of-the-art methods.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. Drost, B., Ulrich, M., Navab, N., Ilic, S.: Model globally, match locally: efficient and robust 3D object recognition. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 998–1005. IEEE (2010)

    Google Scholar 

  2. Birdal, T., Ilic, S.: Point pair features based object detection and pose estimation revisited. In: 2015 International Conference on 3D Vision (3DV), pp. 527–535. IEEE (2015)

    Google Scholar 

  3. Hinterstoisser, S., et al.: Gradient response maps for real-time detection of textureless objects. IEEE Trans. Pattern Anal. Mach. Intell. 34(5), 876–888 (2012)

    Article  Google Scholar 

  4. 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 

  5. Hinterstoisser, S., Lepetit, V., Rajkumar, N., Konolige, K.: Going further with point pair features. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 834–848. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_51

    Chapter  Google Scholar 

  6. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, ICRA 2009, pp. 3212–3217. IEEE (2009)

    Google Scholar 

  7. Johnson, A.E.: Spin-images: a representation for 3-D surface matching. Diss. Carnegie Mellon University (1997)

    Google Scholar 

  8. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6313, pp. 356–369. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_26

    Chapter  Google Scholar 

  9. Guo, Y., et al.: Rotational projection statistics for 3D local surface description and object recognition. Int. J. Comput. Vis. 105(1), 63–86 (2013)

    Article  MathSciNet  Google Scholar 

  10. 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 

  11. Brachmann, E., et al.: Uncertainty-driven 6D pose estimation of objects and scenes from a single RGB image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  12. Drost, B., Ilic, S.: 3D object detection and localization using multimodal point pair features. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT). IEEE (2012)

    Google Scholar 

  13. Gupta, S., et al.: Inferring 3D object pose in RGB-D images. arXiv preprint arXiv:1502.04652 (2015)

  14. Krull, A., Brachmann, E., Michel, F., Yang, M.Y., Gumhold, S., Rother, C.: Learning analysis-by-synthesis for 6D pose estimation in RGB-D images. In: International Conference on Computer Vision (2015)

    Google Scholar 

  15. Kim, E., Medioni, G.: 3D object recognition in range images using visibility context. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2011)

    Google Scholar 

  16. Choi, C., et al.: Voting-based pose estimation for robotic assembly using a 3D sensor. In: 2012 IEEE International Conference on Robotics and Automation (ICRA). IEEE (2012)

    Google Scholar 

  17. Hartley, R., et al.: Rotation averaging. Int. J. Comput. Vis. 103(3), 267–305 (2013)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

This research was supported by the National Nature Science Foundation of China (Grant nos. 51575332 and 91648202) and the Key Research Project of Ministry of Science and Technology (Grant No. 2017YFB1301503).

Author information

Authors and Affiliations

  1. Robotics Institute, Shanghai Jiao Tong University, No. 800 Dongchuan Road, Minhang District, Shanghai, 200240, China

    Yuke Zhu & Limin Zhu

  2. School of Mechatronic Engineering and Automation, Shanghai University, No. 99 Shangda Road, BaoShan District, Shanghai, 200444, China

    Xu Zhang & Yongkai Cai

  3. HUST-Wuxi Research Institute, No. 329 YanXin Road, Huishan District, Wuxi, 214100, China

    Yuke Zhu, Xu Zhang & Yongkai Cai

Authors
  1. Yuke Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xu Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Limin Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongkai Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xu Zhang .

Editor information

Editors and Affiliations

  1. University of Newcastle, Callaghan, New South Wales, Australia

    Zhiyong Chen

  2. University of Newcastle, Callaghan, New South Wales, Australia

    Alexandre Mendes

  3. University of Newcastle, Callaghan, New South Wales, Australia

    Yamin Yan

  4. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Shifeng Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhu, Y., Zhang, X., Zhu, L., Cai, Y. (2018). Point Pair Features Based Object Recognition with Improved Training Pipeline. In: Chen, Z., Mendes, A., Yan, Y., Chen, S. (eds) Intelligent Robotics and Applications. ICIRA 2018. Lecture Notes in Computer Science(), vol 10985. Springer, Cham. https://doi.org/10.1007/978-3-319-97589-4_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-97589-4_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97588-7

  • Online ISBN: 978-3-319-97589-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation