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Reduction of Point Cloud Artifacts Using Shape Priors Estimated with the Gaussian Process Latent Variable Model

  • Jens KrenzinEmail author
  • Olaf HellwichEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)

Abstract

We present a method that removes point cloud artifacts like noisy points, missing data and outliers from a point cloud using a learned shape prior. The shape prior is learned with the Gaussian Process Latent Variable Model from a set of reference objects. As input data our method uses the estimated object pose from an object detector and a segmented point cloud. We show that the estimated shape prior is capable of modeling fine details to a certain degree. We also show that after applying our method the measured accuracy and completeness is increasing.

Keywords

Point Cloud Discrete Cosine Transform Object Class Discrete Cosine Transform Coefficient Latent Variable Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Ahmed, N., Natarajan, T., Rao, K.R.: Discrete cosine transfom. IEEE Trans. Comput. 23(1), 90–93 (1974)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Bao, S.Y.Z., Chandraker, M., Lin, Y., Savarese, S.: Dense object reconstruction with semantic priors. In: CVPR, pp. 1264–1271. IEEE (2013)Google Scholar
  3. 3.
    Berger, M., Tagliasacchi, A., Seversky, L.M., Alliez, P., Levine, J.A., Sharf, A., Silva, C.: State of the art in surface reconstruction from point clouds. In: Eurographics STAR (Proceedings of EG 2014) (2014)Google Scholar
  4. 4.
    Dame, A., Prisacariu, V.A., Ren, C.Y., Reid, I.D.: Dense reconstruction using 3D object shape priors. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, pp. 1288–1295, 23–28 June 2013Google Scholar
  5. 5.
    Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)CrossRefGoogle Scholar
  6. 6.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards internet-scale multi-view stereo. In: CVPR (2010)Google Scholar
  7. 7.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2010)CrossRefGoogle Scholar
  8. 8.
    Gal, R., Shamir, A., Hassner, T., Pauly, M., Or, D.C.: Surface reconstruction using local shape priors. In: Belyaev, A., Garland, M. (eds.) Geometry Processing. The Eurographics Association (2007)Google Scholar
  9. 9.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. J. Educ. Psychol. 24, 417–441 (1933)CrossRefzbMATHGoogle Scholar
  10. 10.
    Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aanæs, H.: Large scale multi-view stereopsis evaluation. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 406–413. IEEE (2014)Google Scholar
  11. 11.
    Jones, M.W., Baerentzen, J.A., Sramek, M.: 3D distance fields: a survey of techniques and applications. IEEE Trans. Vis. Comput. Graph. 12(4), 581–599 (2006)CrossRefGoogle Scholar
  12. 12.
    Kazhdan, M.M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. 32(3), 29 (2013)CrossRefzbMATHGoogle Scholar
  13. 13.
    Lawrence, N.D.: Probabilistic non-linear principal component analysis with Gaussian process latent variable models. J. Mach. Learn. Res. 6, 1783–1816 (2005)MathSciNetzbMATHGoogle Scholar
  14. 14.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: SIGGRAPH, pp. 163–169 (1987)Google Scholar
  15. 15.
    Maurer Jr., C.R., Qi, R., Raghavan, V.: A linear time algorithm for computing exact Euclidean distance transforms of binary images in arbitrary dimensions. IEEE Trans. Pattern Anal. Mach. Intell. 25(2), 265–270 (2003)CrossRefGoogle Scholar
  16. 16.
    Moons, T., Gool, L.J.V., Vergauwen, M.: 3D reconstruction from multiple images: Part 1 - principles. Found. Trends Comput. Graph. Vis. 4(4), 287–404 (2009)CrossRefGoogle Scholar
  17. 17.
    Nan, L., Xie, K., Sharf, A.: A search-classify approach for cluttered indoor scene understanding. ACM Trans. Graph. 31(6), 137:1–137:10 (2012)CrossRefGoogle Scholar
  18. 18.
    Pauly, M., Mitra, N.J., Giesen, J., Gross, M., Guibas, L.J.: Example-based 3D scan completion. In: Proceedings of the Third Eurographics Symposium on Geometry Processing. SGP 2005, Eurographics Association, Aire-la-Ville, Switzerland (2005)Google Scholar
  19. 19.
    Pearson, K.: On lines and planes of closest fit to systems of points in space. Phil. Mag. 2, 559–572 (1901)CrossRefzbMATHGoogle Scholar
  20. 20.
    Prisacariu, V.A., Reid, I.D.: Nonlinear shape manifolds as shape priors in level set segmentation and tracking. In: The 24th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, pp. 2185–2192, 20–25 June 2011Google Scholar
  21. 21.
    Prisacariu, V.A., Segal, A.V., Reid, I.: Simultaneous monocular 2D segmentation, 3D pose recovery and 3D reconstruction. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 593–606. Springer, Heidelberg (2013)Google Scholar
  22. 22.
    Ren, C.Y., Prisacariu, V., Reid, I.: Regressing local to global shape properties for online segmentation and tracking. Int. J. Comput. Vision 106(3), 269–281 (2013)CrossRefGoogle Scholar
  23. 23.
    Seitz, S., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. 1, pp. 519–526. IEEE Computer Society, New York (2006)Google Scholar
  24. 24.
    Shao, T., Xu, W., Zhou, K., Wang, J., Li, D., Guo, B.: An interactive approach to semantic modeling of indoor scenes with an RGBD camera. ACM Trans. Graph 31(6), 136:1–136:11 (2012)CrossRefGoogle Scholar
  25. 25.
    Shen, C.H., Fu, H., Chen, K., Hu, S.M.: Structure recovery by part assembly. ACM Trans. Graph. 31(6), 180: 1–180: 11 (2012)CrossRefGoogle Scholar
  26. 26.
    Wang, Q., Wang, F., Li, D., Wang, X.: Clustering-based latent variable models for monocular non-rigid 3D shape recovery. In: Huang, D.-S., Jo, K.-H., Wang, L. (eds.) ICIC 2014. LNCS, vol. 8589, pp. 162–172. Springer, Heidelberg (2014)Google Scholar
  27. 27.
    Wu, C.: VisualSFM: a visual structure from motion system (2016). http://ccwu.me/vsfm/. Accessed 11 Feb 2016
  28. 28.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Computer Vision & Remote SensingTU BerlinBerlinGermany

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