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access icon free Multiscale bilateral filtering to detect 3D interest points

The detection of 3D interest points is a central problem in computer graphics, computer vision, and pattern recognition. It is also an important preprocessing step in the analysis of 3D model matching. Although studied for decades, detecting 3D interest points remains a challenge. In this study, a novel multiscale bilateral filtering method is presented to detect 3D interest points. This method first simplifies repeatedly the input 3D mesh to form k multiresolution meshes. For each mesh, on the basis of the computed saliency of the mesh vertex, the bilateral filtering is used to remove the noise of the mesh saliencies and the global contrast to normalise the saliencies, and then the interest points are extracted on the basis of the normalised saliency. The proposed method then gathers and clusters all interest points detected on the k multiresolution meshes, and the centres of these clusters are treated as the final interest points. In this method, both the spatial closeness and the geometric similarities of the mesh vertices are considered during the bilateral filtering process. The experimental results validate the effectiveness of the proposed method to detect 3D interest points. This method is also tested the potential to distinguish 3D models.

References

    1. 1)
      • 5. Bai, X., Latecki, L.J., Liu, W.Y.: ‘Skeleton pruning by contour partitioning with discrete curve evolution’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (3), pp. 449462.
    2. 2)
      • 33. Bertsekas, D.P.: ‘A new algorithm for the assignment problem’, Math. Program., 1981, 21, (1), pp. 152171.
    3. 3)
      • 28. Liu, X.Y., Ma, L.Z., Liu, L.G.: ‘P2: a robust and rotationally invariant shape descriptor with applications to mesh saliency’, Appl. Math., J. Chin. Univ., 2016, 31, (1), pp. 5367.
    4. 4)
      • 22. Smith, S.M., Brady, J.M.: ‘SUSAN–a new approach to low level image processing’, Int. J. Comput. Vis., 1997, 23, (1), pp. 4578.
    5. 5)
      • 18. Novatnack, J., Nishino, K.: ‘Scale-dependent 3D geometric features’. Int. Conf. on Computer Vision, Rio de Janeiro, Brazil, 2007, pp. 18.
    6. 6)
      • 34. Giorgi, D., Biasotti, S., Paraboschi, L.: ‘Shape retrieval contest 2007: watertight models track’. SHREC Competition, Genoa, Italy, 2007, 8, (7), pp. 111.
    7. 7)
      • 16. Castellani, U., Cristani, M., Fantoni, S., et al: ‘Sparse points matching by combining 3D mesh saliency with statistical descriptors’, Comput. Graph. Forum, 2008, 27, (2), pp. 643652.
    8. 8)
      • 19. Aubry, M., Schlickewei, U., Cremers, D.: ‘The wave kernel signature: a quantum mechanical approach to shape analysis’. IEEE Int. Conf. on Computer Vision Workshops, Barcelona, Spain, 2011, pp. 16261633.
    9. 9)
      • 7. Dutagaci, H., Cheung, C.P., Godil, A.: ‘Evaluation of 3D interest point detection techniques via human-generated ground truth’, Vis. Comput., 2012, 28, (9), pp. 901917.
    10. 10)
      • 26. Garland, M., Heckbert, P.S.: ‘Surface simplification using quadric error metrics’. Proc. of the 24th Annual Conf. on Computer Graphics and Interactive Techniques, New York, USA, 1997, pp. 209216.
    11. 11)
      • 1. Ruggeri, M.R., Patanè, G., Spagnuolo, M., et al: ‘Spectral-driven isometry invariant matching of 3D shapes’, Int. J. Comput. Vis., 2010, 89, (2), pp. 248265.
    12. 12)
      • 20. Song, R., Liu, Y., Martin, R.R., et al: ‘3D point of interest detection via spectral irregularity diffusion’, Vis. Comput., 2013, 29, (6–8), pp. 695705.
    13. 13)
      • 17. Harris, C., Stephens, M.: ‘A combined corner and edge detector’. Proc. Alvey Vision Conf., Manchester, UK, 1988, no. 3, pp. 147151.
    14. 14)
      • 29. Cheng, M.M., Mitra, N.J., Huang, X.L., et al: ‘Global contrast based salient region detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2015, 37, (3), pp. 569582.
    15. 15)
      • 10. Sun, J., Ovsjanikov, M., Guibas, L.: ‘A concise and provably informative multiscale signature based on heat diffusion’, Comput. Graph. Forum, 2009, 28, (5), pp. 13831392.
    16. 16)
      • 11. Kim, V.G., Lipman, Y., Funkhouser, T.: ‘Blended intrinsic maps’, ACM Trans. Graph., 2011, 30, (4), p. 79.
    17. 17)
      • 6. Katz, S., Leifman, G., Tal, A.: ‘Mesh segmentation using feature point and core extraction’, Vis. Comput., 2005, 21, (8), pp. 649658.
    18. 18)
      • 23. Zhang, M., Gunturk, B.K.: ‘Multiresolution bilateral filtering for image denoising’, IEEE Trans. Image Process., 2008, 17, (12), pp. 23242333.
    19. 19)
      • 32. Lin, X., Zhu, C., Zhang, Q., et al: ‘3D interest point detection based on geometric measures and sparse refinement’. IEEE Int. Workshop on Multimedia Signal Processing, Luton, UK, 2017.
    20. 20)
      • 13. Lee, C.H., Varshney, A., Jacobs, D.W.: ‘Mesh saliency’, ACM Trans. Graph., 2005, 24, (3), pp. 659666.
    21. 21)
      • 24. Nociar, M., Ferko, A.: ‘Feature-preserving mesh denoising via attenuated bilateral normal filtering and quadrics’. Proc. of the 26th Spring Conf. on Computer Graphics, Budmerice, Slovakia, 2010, pp. 149156.
    22. 22)
      • 14. Godil, A., Wagan, A.I.: ‘Salient local 3D features for 3D shape retrieval’. IS&T/SPIE Electronic Imaging, San Francisco, California, 2011, pp. 78640S78640S.
    23. 23)
      • 21. Chen, X., Saparov, A., Pang, B., et al: ‘Schelling points on 3D surface meshes’, ACM Trans. Graph., 2012, 31, (4), p. 29.
    24. 24)
      • 25. Sylvain, P., Pierre, K., Jack, T., et al: ‘Bilateral filtering: theory and applications’, Found. Trends Comput. Graph. Vis., 2009, 4, (1), pp. 174.
    25. 25)
      • 12. Gelfand, N., Mitra, N.J., Guibas, L.J., et al: ‘Robust global registration’. Symp. on Geometry Processing, Vienna, Austria, 2005, vol. 2, no. 3, pp. 197206.
    26. 26)
      • 30. Shen, H., Miao, X.D., Tan, Y.S.: ‘Visual saliency detection based on deredundancy and global contrast’, Appl. Mech. Mater., 2013, 246, (6), pp. 208212.
    27. 27)
      • 4. Zou, G., Hua, J., Dong, M., et al: ‘Surface matching with salient keypoints in geodesic scale space’, Comput. Animat. Virtual Worlds, 2008, 19, (3), pp. 399410.
    28. 28)
      • 15. Darom, T., Keller, Y.: ‘Scale-invariant features for 3-D mesh models’, IEEE Trans. Image Process., 2012, 21, (5), pp. 27582769.
    29. 29)
      • 2. Ali, S.S.A., Mohammed, B., Farid, B.: ‘Keypoints-based surface representation for 3d modeling and 3D object recognition’, Pattern Recognit., 2017, 64, pp. 2938.
    30. 30)
      • 3. Hu, J., Hua, J.: ‘Salient spectral geometric features for shape matching and retrieval’, Vis. Comput., 2009, 25, (5), pp. 667675.
    31. 31)
      • 9. Leizer, T., Philippos, M.: ‘3D interest point detection via discriminative learning’. European Conf. on Computer Vision, Zurich, Switzerland, 2014, pp. 159173.
    32. 32)
      • 27. Sethian, J.A., Vladimirsky, A.: ‘Fast methods for the Eikonal and related Hamilton-Jacobi equations on unstructured meshes’, Proc. Natl. Acad. Sci., 2000, 97, (11), pp. 56995703.
    33. 33)
      • 8. Sipiran, I., Bustos, B.: ‘Harris 3D: a robust extension of the Harris operator for interest point detection on 3D meshes’, Vis. Comput., 2011, 27, (11), pp. 963976.
    34. 34)
      • 31. A Benchmark for 3D Interest Point Detection Algorithms. Available at http://www.itl.nist.gov/iad/vug/sharp/benchmark/3DInterestPoint/.
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