Diese Präsentation wurde erfolgreich gemeldet.
Wir verwenden Ihre LinkedIn Profilangaben und Informationen zu Ihren Aktivitäten, um Anzeigen zu personalisieren und Ihnen relevantere Inhalte anzuzeigen. Sie können Ihre Anzeigeneinstellungen jederzeit ändern.
Nächste SlideShare
×

# Shape from Distortion - 3D Digitization

1.260 Aufrufe

Veröffentlicht am

3D digitization, reconstruction of an object by shape from distortion using Tarini Method and Simple Deflectometry

Veröffentlicht in: Technologie, Kunst & Fotos
• Full Name
Comment goes here.

Are you sure you want to Yes No
• Als Erste(r) kommentieren

### Shape from Distortion - 3D Digitization

1. 1. BY:Agam A. NugrohoVanya V.Valindria Eng Wei Yong VIBOT 4 2011
2. 2.  Introduction Methods Procedure Tarini Method Simple Deflectometry Result Conclusion
3. 3.  3D image reconstruction  main issues in computer vision. Many technique:  Shading  Texture  Stereoscopy  Structured Light  Contour Shape from distortion  reconstruct the 3D shape of the mirror from its reflected images
4. 4.  Obtain the relationship that is useful in 3D surface reconstruction for specular objects. 3D surface normal map depth map
5. 5.  Deduce 3D shape of the target object by looking at the way it distorts patterns from a monitor.
6. 6. This method works by measuring a surfaceslope of an optical beam which is deflected bythe surface.
7. 7.  Devices:  Monitor: DELL 14” , flat monitor  Camera: UI-1225 LE-C. CMOS 1/3”. 752 x 480  Object : Small specular object
8. 8.  Bouget toolbox using checkerboard Parameter ValueFocal Length fc = [ 4017.658 3145.87 ] ± [ 267.5 380.1]Principal Point cc = [ 375.5 239.5 ] ± [ 0 0 ]Skew alpha_c = 0 => angle of pixel axes = 90 degreesDistortion kc = [ -0.88 -30.02 0.01 0 0 ] ± [ 1.57 134 0.03 0.01 0]Pixel Error err = [ 2.3 1.9 ]
9. 9. 50300 Duplicate in row250 100 and column200 150150 200100 25050 0 0 20 40 60 80 100 120 140 300 1 stripe pattern 350 400 450 20 40 60 80 100120
10. 10. Vertical pattern Horizontal pattern50100150200250300 Diagonal 45 pattern Diagonal 135 pattern350400450 20 40 60 80 100120
11. 11.  Using Specular Object Fornormali-zation Other orientation and positions
12. 12.  Using perfect mirror object  Normalization
13. 13. Which stripes( y)??! Diophantine Equations:
14. 14. Clipped Peak SaturationCurve from 1 line
15. 15.  Exposure_time = 10.5 Pixel_clock = 30; Frame rate = max 250 200 150 100 50 0 0 20 40 60 80 100 120 140 160 180 200 Curve from 1 line
16. 16.  Color projected by screen ≠ perceived by camera Generate Ramp Images in RGB Red Ramp Green Ramp Blue Ramp
17. 17.  Project ramp images to mirror object Red Ramp Green Ramp Blue Ramp
18. 18.  Response Curve for each ramp image RED Ramp Image RED response curve : linear 160 250 160 140 140 200 120 120 100 100 150 80 80 100 60 60 40 40 50 20 20 0 0 0 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 Noisy response from other channels
19. 19.  Find minimum and maximum values of perceived color values in each channel MIN MAX Red 17 132 Green 26 182 Blue 21 247
20. 20. Normalization of the linear Response Curve Response Curve from Red Channel Response Curve from Green Channel Response Curve from Blue Channel140 200 250120 200 150100 15080 10060 10040 50 5020 0 0 0 0 50 100 150 200 250 0 50 100 150 200 250 0 50 100 150 200 Normalized Response Curve - RED Normalized Response Cruve - BLUE Normalized Response Curve - GREEN300 300 300250 250 250200 200 200150 150 150100 100 10050 50 50 0 0 0 0 50 100 150 200 250 0 50 100 150 200 250 0 50 100 150 200
21. 21. • Vertical pattern with 1˚ angle increment• Obvious pattern shift observed Θ=0 Θ=1 Θ=2 Θ=3 Θ=4 Θ=5
22. 22. Theta = 0 Theta = 1 Theta = 2 Theta = 3 Theta = 4250 250 250 250 300200 200 200 200 200150 150 150 150100 100 100 100 10050 50 50 50 0 0 0 0 0 0 200 400 0 200 400 0 200 400 0 200 400 0 200 400 Theta = 5 Theta = 6 Theta = 7 Theta = 8 Theta = 9300 300 300 300 300200 200 200 200 200100 100 100 100 100 0 0 0 0 0 0 200 400 0 200 400 0 200 400 0 200 400 0 200 400 Theta = 10 Theta = 11 Theta = 12 Theta = 13300 300 250 250 200 200200 200 150 150 100 100100 100 50 50 0 0 0 0 0 200 400 0 200 400 0 200 400 0 200 400
23. 23. Theta = 0 Theta = 1 Theta = 2 Theta = 3 Theta = 4250 250 250 250 300200 200 200 200 200150 150 150 150100 100 100 100 10050 50 50 50 0 0 0 0 0 0 200 400 0 200 400 0 200 400 0 200 400 0 200 400 Theta = 5 Theta = 6 Theta = 7 Theta = 8 Theta = 9300 300 300 300 300200 200 200 200 200100 100 100 100 100 0 0 0 0 0 0 200 400 0 200 400 0 200 400 0 200 400 0 200 400 Theta = 10 Theta = 11 Theta = 12 Theta = 13300 300 250 250 200 200200 200 150 150 100 100100 100 50 50 0 0 0 0 0 200 400 0 200 400 0 200 400 0 200 400
24. 24. Angle α1 1.692 1.843 1.784 1.875 1.546 1.557 1.618 1.819 1.8410 1.6511 1.5312 1.6913 1.84
25. 25. θ= 1o α = 1.7 rad δ= 97 pixels
26. 26.  3D reconstruction of specular surface can be performed using shape from distortion method In this project, we succeed to obtain the orientation-shift relationship using the flat surface This result will be useful for extracting the depth from the specular surface In the future it can be extend to a more complex shiny surface 3D reconstruction.
27. 27. THANK YOU….
28. 28.  M.Tarini, et,al, 3D acquisition of mirroring objects using striped patterns, Graphical Models 67 233–259.2005. Hui-Liang Shen, et.al. Estimation of Optoelectronic Conversion Functions of Imaging Devices Without Using Gray Samples, Wiley Periodical. Volume 33, Number 2, April 2008. V. Hanta. SOLUTION OF SIMPLE DIOPHANTINE EQUATIONS BY MEANS OF MATLAB. Institute of Chemical Technology, Prague. Y. Francken. Metostructure Acquisition with Planar Illuminants.PhD Dissertation. University of Maastrich