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Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes

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Abstract

With the increasing amount of 3D data and the ability of capture devices to produce low-cost multimedia data, the capability to select relevant information has become an interesting research field. In 3D objects, the aim is to detect a few salient structures which can be used, instead of the whole object, for applications like object registration, retrieval, and mesh simplification. In this paper, we present an interest points detector for 3D objects based on Harris operator, which has been used with good results in computer vision applications. We propose an adaptive technique to determine the neighborhood of a vertex, over which the Harris response on that vertex is calculated. Our method is robust to several transformations, which can be seen in the high repeatability values obtained using the SHREC feature detection and description benchmark. In addition, we show that Harris 3D outperforms the results obtained by recent effective techniques such as Heat Kernel Signatures.

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References

  1. Bronstein, A., Bronstein, M., Bustos, B., Castellani, U., Crisani, M., Falcidieno, B., Guibas, L.J., Kokkinos, I., Murino, V., Sipiran, I., Ovsjanikov, M., Patane, G., Spagnuolo, M., Sun, J.: SHREC 2010: Robust feature detection and description benchmark. In: Proc. Eurographics Workshop on 3D Object Retrieval, pp. 79–86. Eurographics Association, Aire-la-Ville (2010)

    Google Scholar 

  2. Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. 74(1), 59–73 (2007)

    Article  Google Scholar 

  3. Castellani, U., Cristani, M., Fantoni, S., Murino, V.: Sparse points matching by combining 3D mesh saliency with statistical descriptors. Comput. Graph. Forum 27(2), 643–652 (2008)

    Article  Google Scholar 

  4. Garland, M., Heckbert, P.S.: Surface simplification using quadric error metrics. In: Proc. Int. Conf. and Exhib. on Comput. Graph. and Interact. Tech. SIGGRAPH ’97, pp. 209–216. ACM Press/Addison-Wesley, New York (1997)

    Chapter  Google Scholar 

  5. Gelfand, N., Mitra, N.J., Guibas, L.J., Pottmann, H.: Robust global registration. In: Proc. Eurographics Symposium on Geometry Processing, p. 197. Eurographics Association, Aire-la-Ville (2005)

    Google Scholar 

  6. Glomb, P.: Detection of interest points on 3D data: Extending the Harris operator. In: Computer Recognition Systems 3. Advances in Soft Computing, vol. 57, pp. 103–111. Springer, Berlin (2009)

    Chapter  Google Scholar 

  7. Harris, C., Stephens, M.: A combined corner and edge detection. In: Proc. of The Fourth Alvey Vision Conference, pp. 147–151 (1988)

    Google Scholar 

  8. Ho, H., Gibbins, D.: Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds. IET Comput. Vis. 3(4), 201 (2009)

    Article  MathSciNet  Google Scholar 

  9. Hu, J., Hua, J.: Salient spectral geometric features for shape matching and retrieval. Vis. Comput. 25(5–7), 667–675 (2009)

    Article  Google Scholar 

  10. Hua, J., Lai, Z., Dong, M., Gu, X., Qin, H.: Geodesic distance-weighted shape vector image diffusion. IEEE Trans. Vis. Comput. Graph. 14(6), 1643–1650 (2008)

    Article  Google Scholar 

  11. Katz, S., Leifman, G., Tal, A.: Mesh segmentation using feature point and core extraction. Vis. Comput. 21(8), 649–658 (2005)

    Article  Google Scholar 

  12. Laptev, I.: On space-time interest points. Int. J. Comput. Vis. 64(2–3), 107–123 (2005)

    Article  Google Scholar 

  13. Laptev, I., Pérez, P.: Retrieving actions in movies. In: Int. Conf. in Comput. Vis, pp. 1–8 (2007)

    Google Scholar 

  14. Lee, C.H., Varshney, A., Jacobs, D.W.: Mesh saliency. In: Proc. Int. Conf. and Exhib. on Comput. Graph. and Interact. Tech. SIGGRAPH ’05, pp. 659–666. ACM, New York (2005)

    Google Scholar 

  15. Liu, Y., Zha, H., Qin, H.: Shape topics: A compact representation and new algorithms for 3D partial shape retrieval. In: Proc. IEEE Conf. on Comput. Vis. and Pattern Recognit. CVPR ’06, pp. 2025–2032. IEEE Computer Society, Washington (2006)

    Google Scholar 

  16. Loog, M., Lauze, F.: The improbability of Harris interest points. IEEE Trans. Pattern Anal. Mach. Intell. 32(6), 1141–1147 (2010)

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. 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., Special Issue on 3D Object Retrieval (2009)

  19. Mikolajczyk, K.: Scale & affine invariant interest point detectors. Int. J. Comput. Vis. 60(1), 63–86 (2004)

    Article  Google Scholar 

  20. Novatnack, J., Nishino, K.: Scale-dependent 3D geometric features. In: Proc. Int. Conf. on Comput. Vis., pp. 1–8. IEEE, New York (2007)

    Google Scholar 

  21. Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. Int. J. Comput. Vis. 37(2), 151–172 (2000)

    Article  MATH  Google Scholar 

  22. Shilane, P., Funkhouser, T.: Selecting distinctive 3D shape descriptors for similarity retrieval. In: Proc. IEEE Int. Conf. on Shape Model. and Appl. SMI ’06, Washington, DC, USA, p. 18 (2006)

    Google Scholar 

  23. Sipiran, I., Bustos, B.: A robust 3D interest points detector based on Harris operator. In: Proc. Eurographics Workshop on 3D Object Retrieval, pp. 7–14. Eurographics Association, Aire-la-Ville (2010)

    Google Scholar 

  24. Sun, J., Ovsjanikov, M., Guibas, L.J.: A concise and provably informative multi-scale signature based on heat diffusion. Comput. Graph. Forum 28(5), 1383–1392 (2009)

    Article  Google Scholar 

  25. Tierny, J., Vandeborre, J.P., Daoudi, M.: Enhancing 3D mesh topological skeletons with discrete contour constrictions. Vis. Comput. 24(3), 155–172 (2008)

    Article  Google Scholar 

  26. Zaharescu, A., Boyer, E., Varanasi, K., Horaud, R.P.: Surface feature detection and description with applications to mesh matching. In: Proc. IEEE Conf. on Comput. Vis. and Pattern Recognit. CVPR ’09, Miami Beach, Florida (2009)

    Google Scholar 

  27. Zou, G., Hua, J., Dong, M., Qin, H.: Surface matching with salient keypoints in geodesic scale space. Comput. Animat. Virtual Worlds 19(3–4), 399–410 (2008)

    Article  Google Scholar 

  28. Zou, G., Hua, J., Lai, Z., Gu, X., Dong, M.: Intrinsic geometric scale space by shape diffusion. IEEE Trans. Vis. Comput. Graph. 15(6), 1193–1200 (2009)

    Article  Google Scholar 

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Authors and Affiliations

  1. PRISMA Research Group, Department of Computer Science, University of Chile, Santiago, Chile

    Ivan Sipiran & Benjamin Bustos

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  1. Ivan Sipiran
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  2. Benjamin Bustos
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Correspondence to Ivan Sipiran.

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Sipiran, I., Bustos, B. Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes. Vis Comput 27, 963–976 (2011). https://doi.org/10.1007/s00371-011-0610-y

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