Skip to main content
Springer Nature Link
Log in

A robust 3D unique descriptor for 3D object detection

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

3D object recognition techniques based on local surface features are widely used for robust recognition. This paper proposes a 3D object recognition technique named 3DU using local features computed based on the uniqueness of keypoints. The technique first transforms 3D keypoints into another 3D space using Local Reference Frame. This transformation helps to find a list of probable matched keypoints of a query keypoint. Further, the proposed uniqueness-based descriptor rejects false matches to obtain the best match from the list. The proposed technique is validated by experiments on the Bologna dataset and achieved 100% recognition rate. In real-time scenarios, scenes obtained by an RGBD camera primarily consist of point density variation, cluttered surfaces, and occlusions. Most of the 3D descriptors have not been validated on such scenes in literature. We have analyzed 3DU and top-rated techniques on three RGBD datasets (dataset proposed in this paper, Challenge and Willow datasets). The results obtained by experiments on the proposed dataset show that the top-rated techniques have failed to handle RGBD data and 3DU has outperformed all compared techniques. The inferior performance of all techniques on complex datasets such as Challenge and Willow has elicited a need to develop robust training-free recognition techniques. The proposed dataset and code of the proposed technique 3DU are openly available in Mendeley (anonymously). http://dx.doi.org/10.17632/rfvzy9jn5v.1.

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

Access this article

Log in via an institution

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

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Data availability

The proposed dataset and code of the proposed technique 3DU are openly available in Mendeley.http://dx.doi.org/10.17632/rfvzy9jn5v.1.

References

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

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

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

    Article  Google Scholar 

  4. Akagündüz E, Ulusoy I (2010) 3D object recognition from range images using transform invariant object representation. Electron Lett 46(22):1499–1500

    Article  Google Scholar 

  5. Bayramoglu N, Alatan AA (2010) Shape index SIFT: range image recognition using local features. In: Proceedings of international conference on pattern recognition, pp 352–355

  6. Li X, Chen M, Nie F, Wang Q (2017) A multiview-based parameter free framework for group detection. In: Proceedings of the thirty-first AAAI conference on artificial intelligence, pp 4147–4153

  7. Li X, Chen M, Nie F, Wang Q (2017) Locality adaptive discriminant analysis. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence, IJCAI-17, pp 2201–2207

  8. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: Proceedings of IEEE international conference on computer vision and pattern recognition, vol 1, pp 886–8931

  9. Fergus R, Perona P, Zisserman A (2007) Weakly supervised scale-invariant learning of models for visual recognition. Int J Comput Vis 71(3):273–303

    Article  Google Scholar 

  10. Bosch A, Zisserman A, Munoz X (2007) Image classification using random forests and ferns. In: Proceedings of IEEE 11th international conference on computer vision, pp 1–8

  11. Ferrari V, Jurie F, Schmid C (2010) From images to shape models for object detection. Int J Comput Vis 87(3):284–303

    Article  Google Scholar 

  12. Viola M, Jones MJ, Viola P (2003) Fast multi-view face detection. In: Proceedings of IEEE international conference on computer vision and pattern recognition

  13. Hinterstoisser S, Cagniart C, Ilic S, Sturm P, Navab N, Fua P, Lepetit V (2012) Gradient response maps for real-time detection of textureless objects. IEEE Trans Pattern Anal Mach Intell 34(5):876–888

    Article  Google Scholar 

  14. Guo Y, Bennamoun M, Sohel F, Lu M, Wan J (2014) 3D object recognition in cluttered scenes with local surface features: a survey. IEEE Trans Pattern Anal Mach Intell 36(11):2270–2287

    Article  Google Scholar 

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

    Article  Google Scholar 

  16. Johnson AE, Hebert M (1999) Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans Pattern Anal Mach Intell 21(5):433–449

    Article  Google Scholar 

  17. Rusu RB, Marton ZC, Blodow N, Beetz M (208) Persistent point feature histograms for 3D point clouds. In: Proceedings of the 10th international conference on intelligent autonomous systems

  18. Rusu RB, Blodow N, Beetz M (2009) Fast point feature histograms (FPFH) for 3D registration. In: Proceedings of IEEE international conference on robotics and automation, pp 3212–3217

  19. Marton Z, Pangercic D, Blodow N, Kleinehellefort J, Beetz M (2010) General 3D modelling of novel objects from a single view. In: Proceedings of IEEE international conference on intelligent robots and systems, pp 3700–3705

  20. Tombari F, Salti S, Di Stefano L (2010) Unique signatures of histograms for local surface description. In: European conference on computer vision, pp 356–369

  21. Guo Y, Sohel F, Bennamoun M, Lu M, Wan J (2013) Rotational projection statistics for 3D local surface description and object recognition. Int J Comput Vis 105(1):63–86

    Article  MathSciNet  Google Scholar 

  22. Shah SAA, Bennamoun M, Boussaid F (2017) Keypoints-based surface representation for 3d modeling and 3d object recognition. Pattern Recognit 64:29–38

    Article  Google Scholar 

  23. Li W, Cheng H, Zhang X (2021) Efficient 3D object recognition from cluttered point cloud. Sensors 21(17):5850

    Article  Google Scholar 

  24. Yang J, Zhang Q, Xiao Y, Cao Z (2017) Toldi: an effective and robust approach for 3D local shape description. Pattern Recognit 65:175–187

    Article  Google Scholar 

  25. Zhao H, Tang M, Ding H (2020) Hoppf: a novel local surface descriptor for 3D object recognition. Pattern Recognit 103:107272

    Article  Google Scholar 

  26. Wu L, Zhong K, Li Z, Zhou M, Hu H, Wang C, Shi Y (2021) Pptfh: robust local descriptor based on point-pair transformation features for 3d surface matching. Sensors (Basel, Switzerland) 21:3229

    Article  Google Scholar 

  27. Prakhya SM, Lin J, Chandrasekhar V, Lin W, Liu B (2017) 3DHoPD: a fast low-dimensional 3-D descriptor. IEEE Robot Autom Lett 2:1472–1479

    Article  Google Scholar 

  28. Salti S, Tombari F, Stefano LD (2011) A performance evaluation of 3d keypoint detectors. In: International conference on 3D imaging, modeling, processing, visualization and transmission, pp 236–243

  29. Aldoma A, Fäulhammer T, Vincze M (2014) 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, pp 5016–5023

  30. Mian AS, Bennamoun M, Owens R (2006) Three-dimensional model-based object recognition and segmentation in cluttered scenes. IEEE Trans Pattern Anal Mach Intell 28(10):1584–1601

    Article  Google Scholar 

  31. Taati B, Greenspan M (2011) Local shape descriptor selection for object recognition in range data. Comput Vis Image Underst 115(5):681–694

    Article  Google Scholar 

  32. Bariya P, Novatnack J, Schwartz G, Nishino K (2012) 3D geometric scale variability in range images: features and descriptors. Int J Comput Vis 99(2):232–255

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Indian Institute of Information Technology, Sri City, Chittoor, India

    Piyush Joshi

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

    Alireza Rastegarpanah & Rustam Stolkin

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

    Alireza Rastegarpanah & Rustam Stolkin

Authors
  1. Piyush Joshi
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Alireza Rastegarpanah
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Rustam Stolkin
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Piyush Joshi.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Joshi, P., Rastegarpanah, A. & Stolkin, R. A robust 3D unique descriptor for 3D object detection. Pattern Anal Applic 27, 108 (2024). https://doi.org/10.1007/s10044-024-01326-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10044-024-01326-4

Keywords