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Search inliers based on redundant geometric constraints

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Abstract

This paper presents an efficient correspondence grouping algorithm to search inliers from an initial set of feature matches. The novelty lies in the proposal of a scoring technique for measuring the reliability of a triple combination (three pairs of matches) based on redundant geometric constraints. According to the proposed scoring method, several top-ranking triple combinations are selected for estimating the transformation hypotheses between two 3D shapes. For each transformation hypothesis, a correspondence from a selected correspondence set should cast a vote whether it is satisfying the geometric constraint with it. Finally, the transformation hypothesis with the most votes is considered as the best transformation and the correspondences from the initial correspondence set agreeing with the best transformation are grouped as inliers. We performed both comparative experiments and real application experiments to evaluate the performance of our proposed method on five popular datasets. The experimental results show the superior performance of our method with respect to different levels of noise, point density variation, partial overlap, clutter and occlusion. In addition, our proposed method can boost the performance of a feature-based 3D object recognition algorithm, giving an increase in both high recognition rate and computational efficiency.

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References

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

  2. Besl, P.J., McKay, N.D.: Method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 1611, 586–607 (1992)

    Google Scholar 

  3. Boyer, E., Bronstein, A.M., Bronstein, M.M., Bustos, B., Darom, T., Horaud, R., Hotz, I., Keller, Y., Keustermans, J., Kovnatsky, A.: Shrec 2011: robust feature detection and description benchmark. In: Eurographics Conference on 3D Object Retrieval, pp. 71–78 (2011)

  4. Buch, A.G., Yang, Y., Kruger, N., Petersen, H.G.: In search of inliers: 3D correspondence by local and global voting. In: Computer Vision and Pattern Recognition, pp. 2075–2082 (2014)

  5. Chen, H., Bhanu, B.: 3D Free-Form Object Recognition in Range Images Using Local Surface Patches, pp. 1252–1262. Elsevier, London (2007)

    Google Scholar 

  6. Cho, M., Lee, J., Lee, K.M.: Feature correspondence and deformable object matching via agglomerative correspondence clustering. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1280–1287. IEEE (2009)

  7. Cho, M., Sun, J., Duchenne, O., Ponce, J.: Finding matches in a haystack: a max-pooling strategy for graph matching in the presence of outliers. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2083–2090 (2014)

  8. 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)

  9. Enqvist, O., Josephson, K., Kahl, F.: Optimal correspondences from pairwise constraints. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1295–1302. IEEE (2009)

  10. Ferencz, I.H., Shimshoni, I.: Registration of 3d point clouds using mean shift clustering on rotations and translations. In: 2017 International Conference on 3D Vision (3DV), pp. 374–382. IEEE (2017)

  11. Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, vol. 24. ACM, New York (1981)

    Google Scholar 

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

    Article  Google Scholar 

  13. Guo, Y., Bennamoun, M., Sohel, F., Lu, M., Wan, J., Kwok, N.M.: A comprehensive performance evaluation of 3D local feature descriptors. Int. J. Comput. Vis. 116(1), 66–89 (2016)

    Article  MathSciNet  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  15. Guo, Y., Sohel, F., Bennamoun, M., Wan, J.: An accurate and robust range image registration algorithm for 3D object modeling. IEEE Trans. Multimed. 16(5), 1377–1390 (2014)

    Article  Google Scholar 

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

    Article  Google Scholar 

  17. Leordeanu, M., Hebert, M.: A spectral technique for correspondence problems using pairwise constraints. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 2, pp. 1482–1489. IEEE (2005)

  18. Li, C., Hamza, A.B.: A multiresolution descriptor for deformable 3D shape retrieval. Vis. Comput. 29(6–8), 513–524 (2013)

    Article  Google Scholar 

  19. Lim, J., Lee, K.: 3D object recognition using scale-invariant features. Vis. Comput. 1–14 (2017)

  20. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  21. Lu, R., Zhu, F., Wu, Q., Hao, Y.: Three-dimensional object recognition using an extensible local surface descriptor. Opt. Eng. 56(12), 123109 (2017)

    Article  Google Scholar 

  22. Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3D object retrieval from cluttered scenes. Int. J. Comput. Vis. 89(2–3), 348–361 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Mian, A.S., Bennamoun, M., Owens, R.A.: A novel representation and feature matching algorithm for automatic pairwise registration of range images. Int. J. Comput. Vis. 66(1), 19–40 (2006)

    Article  Google Scholar 

  25. Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  26. Novatnack, J., Nishino, K.: Scale-dependent/invariant local 3D shape descriptors for fully automatic registration of multiple sets of range images. In: European Conference on Computer Vision, pp. 440–453. Springer (2008)

  27. Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)

    Article  Google Scholar 

  28. Rusu, R.B., Blodow, N., Beetz, M.: Fast point feature histograms (FPFH) for 3D registration. In: IEEE International Conference on Robotics and Automation, 2009. ICRA’09, pp. 3212–3217. Citeseer (2009)

  29. Tombari, F., Di Stefano, L.: Object recognition in 3D scenes with occlusions and clutter by Hough voting. In: 2010 Fourth Pacific-Rim Symposium on Image and Video Technology, pp. 349–355. IEEE (2010)

  30. Tombari, F., Salti, S., Di Stefano, L.: Unique signatures of histograms for local surface description. In: European Conference on Computer Vision, pp. 356–369. Springer (2010)

  31. Tombari, F., Salti, S., Stefano, L.D.: Performance evaluation of 3D keypoint detectors. Int. J. Comput. Vis. 102(1–3), 198–220 (2013)

    Article  Google Scholar 

  32. Yang, J., Xian, K., Xiao, Y., Cao, Z.: Performance evaluation of 3D correspondence grouping algorithms. In: 2017 International Conference on 3D Vision (3DV), pp. 467–476. IEEE (2017)

  33. Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3D object recognition. In: 2009 IEEE 12th International Conference on Computer Vision Workshops (ICCV Workshops), pp. 689–696. IEEE (2009)

  34. Zhou, W., Ma, C., Yao, T., Chang, P., Zhang, Q., Kuijper, A.: Histograms of Gaussian normal distribution for 3D feature matching in cluttered scenes. Vis. Comput. 1–17 (2018). https://doi.org/10.1007/s00371-018-1478-x

    Article  Google Scholar 

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Acknowledgements

The authors would like to acknowledge the Stanford 3D Scanning Repository, the University of Western Australia and the University of Bologna for providing their datasets. We also thank Dr. Jiaqi Yang for sharing the code to us. This work is jointly supported by the NSFC (U1713216) and Autonomous subject of the State Key Laboratory of Robotics (2017-Z21).

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Authors and Affiliations

  1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, 110016, China

    Rongrong Lu, Feng Zhu, Qingxiao Wu & Xingyin Fu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Rongrong Lu & Xingyin Fu

  3. Key Laboratory of Opto-Electronic Information Processing, CAS, Shenyang, 110016, China

    Rongrong Lu, Feng Zhu, Qingxiao Wu & Xingyin Fu

  4. The Key Lab of Image Understanding and Computer Vision, Shenyang, 110016, Liaoning Province, China

    Rongrong Lu, Feng Zhu, Qingxiao Wu & Xingyin Fu

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  1. Rongrong Lu
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  2. Feng Zhu
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Correspondence to Rongrong Lu.

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Lu, R., Zhu, F., Wu, Q. et al. Search inliers based on redundant geometric constraints. Vis Comput 36, 253–266 (2020). https://doi.org/10.1007/s00371-018-1605-8

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