Journal of Intelligent & Robotic Systems

, Volume 77, Issue 1, pp 149–170 | Cite as

An Extended Evaluation of Open Source Surface Reconstruction Software for Robotic Applications

  • Thomas WiemannEmail author
  • Hendrik Annuth
  • Kai Lingemann
  • Joachim Hertzberg


Polygonal surface reconstruction is a growing field of interest in mobile robotics. Recently, several open source surface reconstruction software packages have become publicly available. This paper presents an extensive evaluation of several of such packages, with emphasis on their usability in robotic applications. The main aspects of the evaluation are run time, accuracy and topological correctness of the generated polygon meshes.


Mapping Surface Reconstruction Meshing Normal Estimation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Amenta, N., Choi, S., Kolluri, R.K.: The power crust. In: Proceedings of the 6th ACM Symposium on Solid Modeling and Applications (SMA ’01), pp. 249–266. ACM, NY, USA (2001)Google Scholar
  2. 2.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891–923 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  3. 3.
    Barber, C.B., Dobkin, D.P., Huhdanpaa, H.: The quickhull algorithm for convex hulls. ACM Trans. Math. Softw. 22(4), 469–483 (1996) CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Berkmann, J., Caelli, T.: Computation of surface geometry and segmentation using covariance techniques. IEEE Trans. Pattern Anal. Mach. Intell. 16(11), 1114–1116 (1994)CrossRefGoogle Scholar
  5. 5.
    Bernardini, F., Mittleman, J., Rushmeier, H., Silva, C., Taubin, G.: The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Vis. Comput. Graph. 5(4), 349–359 (1999)CrossRefGoogle Scholar
  6. 6.
    Cazals, F., Giesen, J.: Delaunay triangulation based surface reconstruction: Ideas and algorithms. In: Effective Computational Geometry for Curves and Surfaces, pp. 231–273. Springer (2006)Google Scholar
  7. 7.
    CGAL: Computational Geometry Algorithms Library (2012).
  8. 8.
    Cignoni, P., Corsini, M., Ranzuglia, G.: Meshlab: an open-source 3d mesh processing system. ERCIM News 73, 45–46 (2008) Google Scholar
  9. 9.
    Connor, M., Kumar, P.: Fast construction of k-nearest neighbor graphs for point clouds. IEEE Trans. Vis. Comput. Graph. 16(4), 599–608 (2010)CrossRefGoogle Scholar
  10. 10.
    Elseberg, J., Magnenat, S., Siegwart, R., Nüchter, A.: Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration. J. Softw. Eng. Robot. (JOSER) 3(1), 2–12 (2012)Google Scholar
  11. 11.
    Garland, M., Heckbert, P.: Surface simplification using quadric error metrics. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’97), pp. 209–216. ACM Press/Addison-Wesley Publishing Co. (1997)Google Scholar
  12. 12.
    Van Gelder, A., Wilhelms, J.: Topological considerations in isosurface generation. ACM Trans. Graph. 13(4), 337–375 (1994)CrossRefGoogle Scholar
  13. 13.
    Girardeau-Montaut, D., Roux, M., Marc, R., Thibault, G.: Change Detection on Points Cloud Data acquired with a Ground Laser Scanner. In: ISPRS Workshop Laser Scanning (2005).
  14. 14.
    Guennebaud, G., Gross, M.: Algebraic point set surfaces. In: ACM SIGGRAPH 2007 papers (2007)Google Scholar
  15. 15.
    Edelsbrunner, H., Mücke, E.P.: Three-Dimensional Alpha Shapes. ACM Trans. Graph. 13, 43–72 (1994)CrossRefzbMATHGoogle Scholar
  16. 16.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. Comput. Graph. 26(2) (1992)Google Scholar
  17. 17.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Mesh optimization. In: Proceedings of the 20th annual conference on Computer graphics and interactive techniques, SIGGRAPH ’93, pp. 19–26. ACM (1993)Google Scholar
  18. 18.
    Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: An efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots (2013)Google Scholar
  19. 19.
    Izadi, S., Newcombe, R.A., Kim, D., Hilliges, O., Molyneaux, D., Hodges, S., Kohli, P., Shotton, J., Davison, A.J., Fitzgibbon, A.: Kinectfusion: Real-time dynamic 3d surface reconstruction and interaction. In: ACM SIGGRAPH 2011 Talks (SIGGRAPH ’11). ACM (2011)Google Scholar
  20. 20.
    Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proceedings of the 4th Eurographics Symposium on Geometry Processing (SGP ’06), pp. 61–70. Eurographics Association (2006)Google Scholar
  21. 21.
    Kobbelt, L.P., Botsch, M., Schwanecke, U., Seidel, H.P.: Feature sensitive surface extraction from volume data. In: Proceedings of the 28th annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’01), pp. 57–66. ACM, NY, USA (2001)Google Scholar
  22. 22.
    Levin, D.: The approximation power of moving least-squares. Math. Comput. 67(224), 1517–1531 (1998)CrossRefzbMATHGoogle Scholar
  23. 23.
    Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: ACM SIGGRAPH (1987)Google Scholar
  24. 24.
    M. Connor, P.K.: The simple, thread-safe approximate nearest neighbor (stann) c++ library (2010).
  25. 25.
    Magnenat, S.: libnabo (2012).
  26. 26.
    Mount, D.M., Arya, S.: Ann: a library for approximate nearest neighbor searching (2010).
  27. 27.
    Muja, M.: Fast library for approximate nearest neighbor (2012).
  28. 28.
    Muja, M., Lowe, D.G.: Fast approximate nearest neighbors with automatic algorithm configuration. In: International Conference on Computer Vision Theory and Application VISSAPP’09), pp. 331–340. INSTICC Press (2009)Google Scholar
  29. 29.
    C++ header-only fork of the flann library for approximate nearest neighbors (2012).
  30. 30.
    Newman, T.S., Yi, H.: A survey of the marching cubes algorithm. Comput. Graph. 30(5) (2006)Google Scholar
  31. 31.
    Nüchter, A., et al.: 3DTK – The 3D Toolkit (2014).
  32. 32.
    Öztireli, A.C., Guennebaud, G., Gross, M.: Feature preserving point set surfaces based on non-linear kernel regression. Comput. Graph. Forum 28(2) (2009)Google Scholar
  33. 33.
    Park, S., Lee, S., Kim, J.: A surface reconstruction algorithm using weighted alpha shapes. In: Wang, L., Jin, Y. (eds.) Fuzzy Systems and Knowledge Discovery, Lecture Notes in Computer Science, vol. 3613, pp 1141–1150. Springer, Berlin Heidelberg (2005), doi: 10.1007/11539506_143 Google Scholar
  34. 34.
    Payne, B.A., Toga, A.W.: Medical imaging: surface mapping brain function on 3d models. IEEE Compututer Graphics and Applications 10(5), 33–41 (1990)CrossRefGoogle Scholar
  35. 35.
    Rinnewitz, K.O., Wiemann, T., Lingemann, K., Hertzberg, J.: Automatic creation and application of texture patterns to 3d polygon maps. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 3691–3696 (2013), doi: 10.1109/IROS.2013.6696883
  36. 36.
    Ros. (2014)
  37. 37.
    Rusu, R., Cousins, S.: 3d is here: point cloud library (pcl). In: IEEE International Conference on Robotics and Automation (ICRA ’11), pp. 1 –4 (2011)Google Scholar
  38. 38.
    Rusu, R.B., Marton, Z.C., Blodow, N., Dolha, M.E., Beetz, M.: Towards 3D point cloud based object maps for household environments. Robotics and Autonomous Systems. Spec. Issue Semant. Knowl. Robot. 56(11), 927–941 (2008)Google Scholar
  39. 39.
    Steinbruecker, F., Sturm, J., Cremers, D.: Real-time visual odometry from dense rgb-d images. In: Workshop on Live Dense Reconstruction with Moving Cameras at the International Conference on Computer Vision (ICCV) (2011)Google Scholar
  40. 40.
    Wiemann, T.: The las vegas surface reconstruction toolkit. (2014)
  41. 41.
    Wiemann, T., Hertzberg, J., Lingemann, K., Annuth, H.: An evaluation of open source surface reconstruction software for robotic applications. In: Proceedings of International Conference On Advanced Robotics (ICAR 2013) (2013)Google Scholar
  42. 42.
    Wiemann, T., Lingemann, K., Hertzberg, J.: Automatic map creation for environment modelling in robotic simulators. In: Proceedings 27th European Conference on Modelling and Simulation (ECMS 2013), pp. 712–718 (2013)Google Scholar
  43. 43.
    Wiemann, T., Lingemann, K., Nüchter, A., Hertzberg, J.: A toolkit for automatic generation of polygonal maps – las vegas reconstruction. In: Proceedings of the 7th German Conference on Robotics (ROBOTIK 2012), pp. 446–451. VDE Verlag, München (2012)Google Scholar
  44. 44.
    Wiemann, T., Nüchter, A., Lingemann, K., Stiene, S., Hertzberg, J.: Automatic construction of polygonal maps from point cloud data. In: IEEE International Workshop on Safety Security and Rescue Robotics (SSRR 2010), pp. 1–6 (2010)Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Thomas Wiemann
    • 1
    Email author
  • Hendrik Annuth
    • 2
  • Kai Lingemann
    • 3
  • Joachim Hertzberg
    • 4
  1. 1.Universität OsnabrückOsnabrückGermany
  2. 2.FH Wedel - University of Applied SciencesWedelGermany
  3. 3.DFKI Robotics Innovation CenterOsnabrückGermany
  4. 4.Universität OsnabrückOsnabrückGermany

Personalised recommendations