Contiguous Patch Segmentation in Pointclouds

  • William NguatemEmail author
  • Helmut MayerEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9796)


An algorithm for Contiguous PAtch Segmentation (CPAS) in 3D pointclouds is proposed. In contrast to current state-of-the-art algorithms, CPAS is robust, scalable and provides a more complete description by simultaneously detecting contiguous patches as well as delineating object boundaries. Our algorithm uses a voxel grid to divide the scene into non-overlapping voxels within which clipped planes are fitted with RANSAC. Using a Dirichlet process mixture (DPM) model of Gaussians and connected component analysis, voxels are clustered into contiguous regions. Finally, we use importance sampling on the convex-hull of each region to obtain the underlying patch and object boundary estimates. For urban scenes, the segmentation represents building walls, ground and roof elements (Fig. 1). We demonstrate the robustness of CPAS using data sets from both image matching and raw LiDAR scans.


Dirichlet Process Contiguous Region Indoor Scene Connected Component Analysis Dirichlet Process Mixture 
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.


  1. 1.
    Aldous, D.J.: Exchangeability and related topics. In: Hennequin, P.L. (ed.) École d’Été de Probabilités de Saint-Flour XIII – 1983. Lecture Notes in Mathematics, pp. 1–198. Springer, Heidelberg (1985)CrossRefGoogle Scholar
  2. 2.
    Cabezas, R., Straub, J., Fisher III., J.W.: Semantically-aware aerial reconstruction from multi-modal data. In: ICCV, pp. 2156–2164 (2015)Google Scholar
  3. 3.
    Chum, O., Matas, J.: Matching with PROSAC - progressive sample consensus. In: CVPR, pp. 220–226 (2005)Google Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. PAMI 24(5), 603–619 (2002)CrossRefGoogle Scholar
  5. 5.
    Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. IJCV 96(1), 1–27 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Demir, I., Aliaga, D.G., Benes, B.: Coupled segmentation and similarity detection for architectural models. SIGGRAPH 34(4), 104:1–104:11 (2015)CrossRefGoogle Scholar
  7. 7.
    Evans, M., Hastings, N., Peacock, B.: von mises distribution. In: Statistical Distributions, pp. 189–191 (2000). Chap. 41Google Scholar
  8. 8.
    Ferguson, T.S.: A bayesian analysis of some nonparametric problems. Ann. Statist. 1(2), 209–230 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Fisher, R.: Dispersion on a sphere. Proc. R. Soc. Lond. A: Math. Phys. Eng. Sci. 217(1130), 295–305 (1953)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Frahm, J.-M., Fite-Georgel, P., Gallup, D., Johnson, T., Raguram, R., Wu, C., Jen, Y.-H., Dunn, E., Clipp, B., Lazebnik, S., Pollefeys, M.: Building Rome on a cloudless day. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 368–381. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Fransens, J., Van Reeth, F.: Hierarchical PCA decomposition of point clouds. In: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT 2006), pp. 591–598 (2006)Google Scholar
  13. 13.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. PAMI 32(8), 1362–1376 (2010)CrossRefGoogle Scholar
  14. 14.
    Golovinskiy, A., Funkhouser, T.: Min-cut based segmentation of point clouds. In: IEEE Workshop on Search in 3D and Video (S3DV) at ICCV, September 2009Google Scholar
  15. 15.
    Görür, D., Rasmussen, C.E.: Dirichlet process gaussian mixture models: choice of the base distribution. J. Comput. Sci. Technol. 25(4), 653–664 (2010)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. PAMI 30(2), 328–341 (2008)CrossRefGoogle Scholar
  17. 17.
    Isack, H., Boykov, Y.: Energy-based geometric multi-model fitting. IJCV 97(2), 123–147 (2011)CrossRefzbMATHGoogle Scholar
  18. 18.
    Kent, T.J.: The fisher-bingham distribution on the sphere. J. Roy. Stat. Soc.: Ser. B (Methodol.) 44(1), 71–80 (1982)MathSciNetzbMATHGoogle Scholar
  19. 19.
    Kuhn, A., Hirschmüller, H., Mayer, H.: Multi-resolution range data fusion for multi-view stereo reconstruction. In: Weickert, J., Hein, M., Schiele, B. (eds.) GCPR 2013. LNCS, vol. 8142, pp. 41–50. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  20. 20.
    Kuhn, A., Mayer, H., Hirschmüller, H., Scharstein, D.: A TV prior for high-quality local multi-view stereo reconstruction. In: Proceedings of the 2014 2nd International Conference on 3D Vision, vol. 01, pp. 65–72 (2014)Google Scholar
  21. 21.
    Lafarge, F., Alliez, P.: Surface reconstruction through point set structuring. In: Proceedings of Eurographics, Girona, Spain (2013)Google Scholar
  22. 22.
    Lafarge, F., Keriven, R., Bredif, M., Vu, H.H.: A hybrid multi-view stereo algorithm for modeling urban scenes. PAMI 35(1), 5–17 (2013)CrossRefGoogle Scholar
  23. 23.
    Lafarge, F., Mallet, C.: Creating large-scale city models from 3D-point clouds: a robust approach with hybrid representation. IJCV 99(1), 69–85 (2012)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Lin, H., Gao, J., Zhou, Y., Lu, G., Ye, M., Zhang, C., Liu, L., Yang, R.: Semantic decomposition and reconstruction of residential scenes from lidar data. SIGGRAPH 32(4) (2013)Google Scholar
  25. 25.
    Lo, A.Y.: On a class of bayesian nonparametric estimates: I. density estimates. Ann. Statist. 12(1), 351–357 (1984)MathSciNetCrossRefzbMATHGoogle Scholar
  26. 26.
    Mayer, H., Bartelsen, J., Hirschmüller, H., Kuhn, A.: Dense 3D reconstruction from wide baseline image sets. In: Real-World Scene Analysis 2011, pp. 285–304 (2012)Google Scholar
  27. 27.
    Meixner, P., Leberl, F.: 3-dimensional building details from aerial photography for internet maps. Remote Sens. 3, 721–751 (2011)CrossRefGoogle Scholar
  28. 28.
    Monszpart, A., Mellado, N., Brostow, G., Mitra, N.: RAPter: rebuilding man-made scenes with regular arrangements of planes. SIGGRAPH (2015)Google Scholar
  29. 29.
    Müller, P., Andrs Quintana, F., Jara, A., Hanson, T.: Bayesian nonparametric data analysis. Springer, Switzerland (2015). Springer Series in StatisticsCrossRefzbMATHGoogle Scholar
  30. 30.
    Neal, R.M.: Markov chain sampling methods for dirichlet process mixture models. Journal of Comput. Graph. Stat. 9(2), 249–265 (2000)MathSciNetGoogle Scholar
  31. 31.
    Nguatem, W., Drauschke, M., Mayer, H.: Roof reconstruction from point clouds using importance sampling. Ann. Photogrammetry, Remote Sens. Spat. Inf. Sci. II–3/W3, 73–78 (2013). City Models, Roads and Traffic (CMRT)Google Scholar
  32. 32.
    Oesau, S., Lafarge, F., Alliez, P.: Planar shape detection and regularization in tandem. Comput. Graph. Forum 35(1), 14 (2015)Google Scholar
  33. 33.
    Papon, J., Abramov, A., Schoeler, M., Wörgötter, F.: Voxel cloud connectivity segmentation - supervoxels for point clouds. In: CVPR, pp. 2027–2034 (2013)Google Scholar
  34. 34.
    Pham, T., Chin, T., Yu, J., Suter, D.: The random cluster model for robust geometric fitting. PAMI 36(2), 1658–1671 (2014)CrossRefGoogle Scholar
  35. 35.
    Raguram, R., Chum, O., Pollefeys, M., Matas, J., Frahm, J.M.: Usac: a universal framework for random sample consensus. PAMI 35(8), 2022–2038 (2013)CrossRefGoogle Scholar
  36. 36.
    Rusu, R.B., Cousins, S.: 3D is here: point cloud library (pcl). In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 1–4. IEEE (2011)Google Scholar
  37. 37.
    Schnabel, R., Wahl, R., Klein, R.: Efficient RANSAC for point-cloud shape detection. Comput. Graph. Forum 26(2), 214–226 (2007)CrossRefGoogle Scholar
  38. 38.
    Straub, J., Chang, J., Freifeld, O., Fisher III, J.W.: A dirichlet process mixture model for spherical data. In: AISTATS (2015)Google Scholar
  39. 39.
    Torr, P.H.S., Zisserman, A.: Mlesac: a new robust estimator with application to estimating image geometry. CVIU 78, 2000 (2000)Google Scholar
  40. 40.
    Verdie, Y., Lafarge, F., Alliez, P.: LOD generation for urban scenes. SIGGRAPH 34(3), 15 (2015)zbMATHGoogle Scholar
  41. 41.
    Vosselman, G.: Point cloud segmentation for urban scene classification. Int. Arch. Photogrammetry, Remote Sens. Spatial Inf. Sci. XL–7/W2(2), 257–262 (2013)CrossRefGoogle Scholar
  42. 42.
    Wahl, R., Schnabel, R., Klein, R.: From detailed digital surface models to city models using constrainted simplification. Photogrammetrie-Fernerkundung-Geoinformation 2008(3), 207–215 (2008)Google Scholar
  43. 43.
    Zhang, X., Li, G., Xiong, Y., He, F.: 3D mesh segmentation using mean-shifted curvature. In: Chen, F., Jüttler, B. (eds.) GMP 2008. LNCS, vol. 4975, pp. 465–474. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Institute of Applied Computer ScienceBundeswehr University MunichNeubibergGermany

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