Image Surround: Automatic Projector Calibration for Indoor Adaptive Projection

  • Radhwan Ben Madhkour
  • Ludovic Burczykowski
  • Matei Mancaş
  • Bernard Gosselin
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 124)


In this paper, we present a system able to calibrate projectors, perform 3D reconstruction and project shadow and textures generated in real-time. The calibration algorithm is based on Heikkila’s camera calibration algorithm. It combines Gray coded structured light patterns projection and a RGBD camera. Any projection surface can be used. Intrinsic and extrinsic parameters are computed without a scale factor uncertainty and any prior knowledge about the projector and the projection surface. The projector calibration is used as a basis to augment the scene with information from the RGBD camera. Shadows are generated with lights. Their position is modified in real-time to follow a user position. The 3D reconstruction is based on the Kinect fusion algorithm. The model of scene is used to apply texture on the scene and to generate correct shadows.


projection calibration tracking scene augmentation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ahn, S.H.: Opengl (2011) (last viewed February 1, 2013)Google Scholar
  2. 2.
    Audet, S., Okutomi, M.: A user-friendly method to geometrically calibrate projector-camera systems. In: Computer Vision and Pattern Recognition Workshop, pp. 47–54 (2009)Google Scholar
  3. 3.
    Madhkour, R.B., Mancas, M., Gosselin, B.: Automatic geometric projector calibration: Application to a 3d real-time visual feedback. In: Proceedings of the 8th International Conference on Computer Vision Theory and Apllications (VISIGRAPP 2013), pp. 420–424 (2013)Google Scholar
  4. 4.
    Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library. O’Reilly (2008)Google Scholar
  5. 5.
    Harrison, C., Benko, H., Omnitouch, A.D.W.: wearable multitouch interaction everywhere. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST 2011), pp. 441–450. ACM (2011)Google Scholar
  6. 6.
    Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge University Press (2004)Google Scholar
  7. 7.
    Heikkila, J.: Geometric camera calibration using circular control points. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(10), 1066–1077 (2000)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: Kinectfusion: real-time 3d reconstruction and interaction using a moving depth camera. In: Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST 2011), pp. 559–568. ACM (2011)Google Scholar
  9. 9.
    Kalal, Z., Matas, J., Mikolajczyk, K.: P-N Learning: Bootstrapping Binary Classifiers by Structural Constraints. In: Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  10. 10.
    Kimura, M., Mochimaru, M., Kanade, T.: Projector calibration using arbitrary planes and calibrated camera. In: Proceedings of the 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2007), pp. 1–2. IEEE Computer Society (2007)Google Scholar
  11. 11.
    Marsh, D.: Applied Geometry for Computer Graphicsand CAD. Springer (2004)Google Scholar
  12. 12.
    OpenNI. Openni user guide (November 2010) (last viewed January 19, 2011)Google Scholar
  13. 13.
    Tardif, J.P., Roy, S., Trudeau, M.: Multi-projectors for arbitrary surfaces without explicit calibration nor reconstruction. In: Proceedings of the Fourth International Conference on 3-D Digital Imaging and Modeling (3DIM 2003), pp. 217–224 (2003)Google Scholar
  14. 14.
    Unity Technologies. Unity rendering engine (2013)Google Scholar
  15. 15.
    Matt Wright. Open sound protocol specification 1.0 (2002)Google Scholar
  16. 16.
    Yamazaki, S., Mochimaru, M., Kanade, T.: Simultaneous self-calibration of a projector and a camera using structured light. In: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2011) - Workshops (Procams 2011), pp. 67–74. IEEE Computer Society (2011)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Radhwan Ben Madhkour
    • 1
  • Ludovic Burczykowski
    • 2
  • Matei Mancaş
    • 1
  • Bernard Gosselin
    • 1
  1. 1.Numiediart InstituteUniversity of MonsMonsBelgium
  2. 2.University of Paris 8Saint-DenisFrance

Personalised recommendations