“Fluorescent immersion range scanning” by Hullin, Fuchs, Ihrke, Seidel and Lensch

  • ©Matthias B. Hullin, Martin Fuchs, Ivo Ihrke, Hans-Peter Seidel, and Hendrik P. A. Lensch




    Fluorescent immersion range scanning



    The quality of a 3D range scan should not depend on the surface properties of the object. Most active range scanning techniques, however, assume a diffuse reflector to allow for a robust detection of incident light patterns. In our approach we embed the object into a fluorescent liquid. By analyzing the light rays that become visible due to fluorescence rather than analyzing their reflections off the surface, we can detect the intersection points between the projected laser sheet and the object surface for a wide range of different materials. For transparent objects we can even directly depict a slice through the object in just one image by matching its refractive index to the one of the embedding liquid. This enables a direct sampling of the object geometry without the need for computational reconstruction. This way, a high-resolution 3D volume can be assembled simply by sweeping a laser plane through the object. We demonstrate the effectiveness of our light sheet range scanning approach on a set of objects manufactured from a variety of materials and material mixes, including dark, translucent and transparent objects.


    1. Ben-Ezra, M., and Nayar, S. 2003. What Does Motion Reveal About Transparency? In Proc. of ICCV’03, vol. 2, 1025–1032. Google ScholarDigital Library
    2. Beraldin, J.-A. 2004. Integration of Laser Scanning and Close-Range Photogrammetry – the Last Decade and Beyond. In Proceedings of the XXth ISPRS Congress, 972–983.Google Scholar
    3. Blais, F. 2004. Review of 20 Years of Range Sensor Development. Journal of Electronic Imaging 13, 1, 231–243.Google ScholarCross Ref
    4. Chen, T., Goesele, M., and Seidel, H.-P. 2006. Mesostructure from Specularity. In Proc. of CVPR ’06, 17–22. Google ScholarDigital Library
    5. Chen, T., Lensch, H. P. A., Fuchs, C., and Seidel, H.-P. 2007. Polarization and Phase-Shifting for 3D Scanning of Translucent Objects. In Proc. of CVPR ’07, 1–8.Google Scholar
    6. Clark, J., Trucco, E., and Wolff, L. B. 1997. Using Light Polarization in Laser Scanning. Image and Vision Computing 15, 1, 107–117. Google ScholarDigital Library
    7. Curless, B., and Levoy, M. 1995. Better Optical Triangulation Through Spacetime Analysis. In Proc. of ICCV’95, 987–994. Google ScholarDigital Library
    8. Davis, J., Yang, R., and Wang, L. 2005. BRDF Invariant Stereo using Light Transport Constancy. In Proc. of ICCV’05, 436–443. Google ScholarDigital Library
    9. Deusch, S., and Dracos, T. 2001. Time resolved 3d passive scalar concentration-field imaging by laser induced fluorescence (LIF) in moving liquids. Meas. Sci. Technol., 12, 188–200.Google ScholarCross Ref
    10. Fuchs, C., Chen, T., Goesele, M., Theisel, H., and Seidel, H.-P. 2007. Density Estimation for Dynamic Volumes. Computers & Graphics 31, 2 (Apr.), 205–211. Google ScholarDigital Library
    11. Hawkins, T., Einarsson, P., and Debevec, P. 2005. Acquisition of Time-Varying Participating Media. In Proc. of ACM SIGGRAPH 2005, ACM, 812–815. Google ScholarDigital Library
    12. Höhle, J. 1971. Reconstruction of the Underwater Object. Photogrammetric Engineering 37, 948–954.Google Scholar
    13. Ihrke, I., Goldluecke, B., and Magnor, M. 2005. Reconstructing the Geometry of Flowing Water. In Proc. of ICCV’05, 1055–1060. Google ScholarDigital Library
    14. Ihrke, I., Kutulakos, K. N., Lensch, H. P. A., Magnor, M., and Heidrich, W. 2008. State of the Art in Transparent and Specular Object Reconstruction. In STAR Proceedings of Eurographics, 87–108.Google Scholar
    15. Jin, H., Soatto, S., and Yezzi, A. J. 2005. Multi-View Stereo Reconstruction of Dense Shape and Complex Appearance. International Journal of Computer Vision 63, 3 (Jul), 175–189. Google ScholarDigital Library
    16. Kutulakos, K. N., and Steger, E. 2008. A Theory of Refractive and Specular 3D Shape by Light-Path Triangulation. International Journal of Computer Vision (IJCV) 76, 1, 13–29. Google ScholarDigital Library
    17. Lorensen, W. E., and Cline, H. E. 1987. Marching cubes: A high resolution 3D surface construction algorithm. In Proc. of ACM SIGGRAPH 87, 163–169. Google ScholarDigital Library
    18. Maas, H.-G. 1995. New Developments in Multimedia Photogrammetry. In Optical 3D Measurement Techniques III, A. Grün and H. Kahmen, Eds. Wichmann Verlag.Google Scholar
    19. Miyazaki, D., and Ikeuchi, K. 2005. Inverse Polarization Raytracing: Estimating Surface Shapes of Transparent Objects. In Proc. of CVPR ’05, vol. 2, 910–917. Google ScholarDigital Library
    20. Morris, N. J. W., and Kutulakos, K. N. 2005. Dynamic Refraction Stereo. In Proc. of ICCV’05, 1573–1580. Google ScholarDigital Library
    21. Morris, N. J. W., and Kutulakos, K. N. 2007. Reconstructing the Surface of Inhomogeneous Transparent Scenes by Scatter-Trace Photography. In Proc. of ICCV’07, 1–8.Google Scholar
    22. Murase, H. 1992. Surface Shape Reconstruction of a Nonrigid Transparent Object Using Refraction and Motion. IEEE Transactions on Pattern Analysis and Machine Intelligence 14, 10 (October), 1045–1052. Google ScholarDigital Library
    23. Narasimhan, S. G., Nayar, S. K., Sun, B., and Koppal, S. J. 2005. Structured Light in Scattering Media. Proc. of ICCV’05 I, 420–427. Google ScholarDigital Library
    24. Nayar, S. K., Krishnan, G., Grossberg, M. D., and Raskar, R. 2006. Fast Separation of Direct and Global Components of a Scene Using High Frequency Illumination. In Proc. of ACM SIGGRAPH 2006, 935–944. Google ScholarDigital Library
    25. Park, J., and Kak, A. C. 2004. Specularity Elimination in Range Sensing for Accurate 3D Modeling of Specular Objects. In Proceedings of 3DPVT’04, 707–714. Google ScholarCross Ref
    26. Park, J., and Kak, C. 2008. 3D Modeling of Optically Challenging Objects. IEEE Trans. on Visualization and Computer Graphics 14, 2 (March/April), 246–262. Google ScholarDigital Library
    27. Remondino, F., and El-Hakim, S. 2006. Image Based 3D Modeling: A Review. The Photogrammetric Record 21, 115, 269–291.Google Scholar
    28. Saito, M., Sato, Y., Ikeuchi, K., and Kashiwagi, H. 1999. Measurement of Surface Orientations of Transparent Objects using Polarization in Highlight. In Proc. of CVPR ’99, vol. 1, 381–386.Google Scholar
    29. Sharpe, J., Ahlgren, U., Perry, P., Hill, B., Ross, A., Hecksher-Sorensen, J., Baldock, R., and Davidson, D. 2002. Optical Projection Tomography as a Tool for 3D Microscopy and Gene Expression Studies. Science 296, 19, 541–545.Google ScholarCross Ref
    30. Thorlabs, Inc. Transmission curve of FEL0550 longpass filter. http://www.thorlabs.com/Thorcat/7600/7672-S01.pdf.Google Scholar
    31. Trifonov, B., Bradley, D., and Heidrich, W. 2006. Tomographic Reconstruction of Transparent Objects. In Proc. of EGSR’06, 51–60. Google ScholarCross Ref
    32. Trucco, E., and Fisher, R. B. 1994. Acquisition of Consistent Range Data Using Local Calibration. In IEEE International Conference on Robotics and Automation, 3410–3415.Google Scholar
    33. TU Graz, Institute of Analytical Chemistry. Database of fluorescent dyes. http://www.fluorophores.org.Google Scholar
    34. Zickler, T., Belhumeur, P. N., and Kriegman, D. J. 2002. Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction. International Journal of Computer Vision (IJCV) 49, 2–3, 215–227. Google ScholarDigital Library

ACM Digital Library Publication:

Overview Page: