Automatic 3D Reconstruction of Indoor Manhattan World Scenes Using Kinect Depth Data

  • Dominik Wolters
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)


This paper discusses a system to reconstruct indoor scenes automatically and evaluates its accuracy and applicability. The focus is on the realization of a simple, quick and inexpensive way to map empty or slightly furnished rooms. The data is acquired with a Kinect sensor mounted onto a pan-tilt head. The Manhattan world assumption is used to approximate the environment. The approach for determining the wall, floor and ceiling planes of the rooms is based on a plane sweep method. The floor plan is reconstructed from the detected planes using an iterative flood fill algorithm. Furthermore, the developed method allows to detect doors and windows, generate 3D models of the measured rooms and to merge multiple scans.


Point Cloud Floor Plan Recording Location Kinect Sensor Multiple Scan 
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.
    Adan, A., Huber, D.: 3D reconstruction of interior wall surfaces under occlusion and clutter. In: International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission (3DIMPVT), pp. 275–281 (2011)Google Scholar
  2. 2.
    Budroni, A., Böhm, J.: Automatic 3D modelling of indoor manhattan-world scenes from laser data. In: Proceedings of the ISPRS Commission V Mid-Term Symposium ‘Close Range Image Measurement Techniques’, Newcastle upon Tyne, UK, vol. XXXVIII, pp. 115–120 (2010)Google Scholar
  3. 3.
    Coughlan, J.M., Yuille, A.L.: Manhattan world: compass direction from a single image by bayesian inference. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, pp. 941–947 (1999)Google Scholar
  4. 4.
    Du, H., Henry, P., Ren, X., Cheng, M., Goldman, D.B., Seitz, S.M., Fox, D.: Interactive 3D modeling of indoor environments with a consumer depth camera. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 75–84 (2011)Google Scholar
  5. 5.
    Gallup, D., Frahm, J.M., Mordohai, P., Yang, Q., Pollefeys, M.: Real-time plane-sweeping stereo with multiple sweeping directions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR ’07), Minneapolis, USA, pp. 1–8, June 2007Google Scholar
  6. 6.
    Hähnel, D., Burgard, W., Thrun, S.: Learning compact 3D models of indoor and outdoor environments with a mobile robot. Robot. Auton. Syst. 44(1), 15–27 (2003)CrossRefGoogle Scholar
  7. 7.
    Johnston, M., Zakhor, A.: Estimating building floor-plans from exterior using laser scanners. In: SPIE Electronic Imaging Conference, 3D Image Capture and Applications, vol. 3 (2008)Google Scholar
  8. 8.
    Neverova, N., Muselet, D., Trémeau, A.: 2\(^\text{1 }\)/\(_\text{2 }\)D scene reconstruction of indoor scenes from single RGB-D images. In: Tominaga, S., Schettini, R., Trémeau, A. (eds.) CCIW 2013. LNCS, vol. 7786, pp. 281–295. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  9. 9.
    Newcombe, R.A., Davison, A.J., Izadi, S., Kohli, P., Hilliges, O., Shotton, J., Molyneaux, D., Hodges, S., Kim, D., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136 (2011)Google Scholar
  10. 10.
    Okorn, B., Xiong, X., Akinci, B., Huber, D.: Toward automated modeling of floor plans. In: Proceedings of the Symposium on 3D Data Processing, Visualization and Transmission, vol. 2 (2010)Google Scholar
  11. 11.
    Rofer, T.: Using histogram correlation to create consistent laser scan maps. In: Proceedings of the IEEE International Conference on Robotics Systems (IROS-2002), Lausanne, pp. 625–630 (2002)Google Scholar
  12. 12.
    Taylor, C., Cowley, A.: Parsing indoor scenes using RGB-D imagery. In: Proceedings of Robotics: Science and Systems. Sydney, July 2012Google Scholar
  13. 13.
    Weiß, G., Wetzler, C., von Puttkamer, E.: Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans. In: Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems ’94. ‘Advanced Robotic Systems and the Real World’, IROS ’94. vol. 1, pp. 595–601 (1994)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Institute of Computer ScienceKiel UniversityKielGermany

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