1. 3D MULTISPECTRAL ACQUISITION Steven Le Moan, Alamin Mansouri Université de Bourgogne, Auxerre, FR Workshop on Multispectral imaging and Biometrics Gjøvik University College – November 2010

2. O UTLINE  Context & goals  3D multispectral scanner  Depth imaging  Space carving  Perspectives

3. C ONTEXT & GOALS  How to describe a real-life object ? Shape, color, touch, smell, sound,..  How does it look like ? Shape + Color >> 3D multispectral

4. C ONTEXT & GOALS  What is a 3D multispectral dataset ? Way too many dimensions ! One “pixel” is defined by: - 3 spatial coordinates (X,Y,Z) - N spectral values (B 1, B 2,..., B N ) >> Multimodal (N+3) dimensional space

5. 3D MULTISPECTRAL SCANNER  System overview Mansouri A., Etude, conception et réalisation d’un système multispectral de vision pour des applications de proximité et développement d’algorithmes de reconstruction de la reflectance, PhD Thesis, 2005

6. 3D MULTISPECTRAL SCANNER  Calibration Caltech Calibration Toolbox for Matlab

7. 3D MULTISPECTRAL SCANNER  Acquisition

8. 3D MULTISPECTRAL SCANNER  Results

9. 3D MULTISPECTRAL SCANNER  Results

10. 3D MULTISPECTRAL SCANNER  Limitations (to be improved) - Lack of convenience - Heavy calibration - What about pearlescence ?

11. D EPTH IMAGING  Principle The depth is printed in the blanket. Page D., Part decomposition of 3D surfaces, PhD thesis, 2003

12. D EPTH IMAGING  LIDAR (LIght Detection And Ranging) - Remote sensing applications - Short wavelengths (UV – nIR)  Time-of-flight cameras - Close-range applications (up to 60m) - “One shot” acquisition

13. D EPTH IMAGING  Examples Roy M., Comparaison et analyse multirésolution de maillages irréguliers avec attributs d’apparence, PhD Thesis, 2004

14. D EPTH IMAGING  Examples Image provided by Norsk Elektro Optikk

15. S PACE CARVING Reconstruction from a set of images

16. P ERSPECTIVES  Convenience (“point & shoot”)  Fast calibration  Unified framework ?

17. T HANK YOU FOR YOUR ATTENTION