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]  Joachim Höhle Reconstruction of the Underwater Object , 1971 .

[2]  William E. Lorensen,et al.  Marching cubes: A high resolution 3D surface construction algorithm , 1987, SIGGRAPH.

[3]  Hiroshi Murase,et al.  Surface Shape Reconstruction of a Nonrigid Transport Object Using Refraction and Motion , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Robert B. Fisher,et al.  Acquisition of consistent range data using local calibration , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[5]  Marc Levoy,et al.  Better optical triangulation through spacetime analysis , 1995, Proceedings of IEEE International Conference on Computer Vision.

[6]  Emanuele Trucco,et al.  Using light polarization in laser scanning , 1997, Image Vis. Comput..

[7]  Katsushi Ikeuchi,et al.  Measurement of surface orientations of transparent objects using polarization in highlight , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[8]  Hans-Gerd Maas,et al.  New developments in Multimedia Photogrammetry , 2001 .

[9]  Themistocles Dracos,et al.  Time resolved 3D passive scalar concentration-field imaging by laser induced fluorescence (LIF) in moving liquids , 2001 .

[10]  J. Vane,et al.  Optical Projection Tomography as a Tool for 3D Microscopy and Gene Expression Studies , 2002 .

[11]  Shree K. Nayar,et al.  What does motion reveal about transparency? , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[12]  Todd E. Zickler,et al.  Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction , 2002, International Journal of Computer Vision.

[13]  J.-Angelo Beraldin,et al.  INTEGRATION OF LASER SCANNING AND CLOSE-RANGE PHOTOGRAMMETRY – THE LAST DECADE AND BEYOND , 2004 .

[14]  Avinash C. Kak,et al.  Specularity elimination in range sensing for accurate 3D modeling of specular objects , 2004 .

[15]  François Blais Review of 20 years of range sensor development , 2004, J. Electronic Imaging.

[16]  Marcus A. Magnor,et al.  Reconstructing the geometry of flowing water , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[17]  Kiriakos N. Kutulakos,et al.  A Theory of Refractive and Specular 3D Shape by Light-Path Triangulation , 2005, ICCV.

[18]  Stefano Soatto,et al.  Multi-View Stereo Reconstruction of Dense Shape and Complex Appearance , 2005, International Journal of Computer Vision.

[19]  Paul Debevec,et al.  Acquisition of time-varying participating media , 2005, SIGGRAPH 2005.

[20]  Katsushi Ikeuchi,et al.  Inverse polarization raytracing: estimating surface shapes of transparent objects , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Kiriakos N. Kutulakos,et al.  Dynamic Refraction Stereo , 2005, ICCV.

[22]  Hans-Peter Seidel,et al.  Mesostructure from Specularity , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[23]  Fabio Remondino,et al.  Image‐based 3D Modelling: A Review , 2006 .

[24]  Ramesh Raskar,et al.  Fast separation of direct and global components of a scene using high frequency illumination , 2006, ACM Trans. Graph..

[25]  Derek Bradley,et al.  Tomographic reconstruction of transparent objects , 2006, SIGGRAPH '06.

[26]  Kiriakos N. Kutulakos,et al.  Reconstructing the Surface of Inhomogeneous Transparent Scenes by Scatter-Trace Photography , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[27]  Hans-Peter Seidel,et al.  Polarization and Phase-Shifting for 3D Scanning of Translucent Objects , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Ruigang Yang,et al.  BRDF Invariant Stereo Using Light Transport Constancy , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Hans-Peter Seidel,et al.  Density estimation for dynamic volumes , 2007, Comput. Graph..

[30]  Avinash C. Kak,et al.  3D Modeling of Optically Challenging Objects , 2008, IEEE Transactions on Visualization and Computer Graphics.

[31]  Shree K. Nayar,et al.  Structured light in scattering media , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[32]  Kiriakos N. Kutulakos,et al.  Transparent and Specular Object Reconstruction , 2010, Comput. Graph. Forum.