shape from distortion - 3d digitization

3D Digitization Project:

Shape from DistortionBY:

Agam A. NugrohoVanya V.Valindria

Eng Wei Yong

VIBOT 42011

Outline

Introduction Methods Procedure Tarini Method Simple Deflectometry

Result Conclusion

Introduction

3D image reconstruction main issues in computer vision.

Many technique: Shading Texture Stereoscopy Structured Light Contour

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Shape from distortion reconstruct the 3D shape of the mirror from its reflected images

Aims

Obtain the relationship that is useful in 3D surface reconstruction for specular objects.

depth map

normal map

3D surface

Tarini Method

Deduce 3D shape of the target object by looking at the way it distorts patterns from a monitor.

Deflectometry

This method works by measuring a surface slope of an optical beam which is deflected by the surface.

Experiment

Devices: Monitor: DELL 14” ,

flat monitor Camera: UI-1225

LE-C. CMOS 1/3”. 752 x 480

Object : Small specular object

Camera Calibration

Bouget toolbox using checkerboardParameter Value

Focal 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 degrees

Distortion kc = [ -0.88 -30.02 0.01 0 0 ] ± [ 1.57 134 0.03 0.01 0]

Pixel Error err = [ 2.3 1.9 ]

Generate Matte Pattern

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1 stripe pattern

Duplicate in row and column

Generate Matte Pattern

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Vertical pattern Horizontal pattern

Diagonal 45 pattern

Diagonal 135 pattern

Project the Matte Patterns Using Specular Object

For normali-zation

Other orientation and positionsX

Matte Extraction

Using perfect mirror object

Normalization

Matte Extraction

Which stripes( y)??!

Diophantine Equations:

Change Method!!

Simple Deflectometry

Experimental Setup - Set Exposure Time

Curve from 1 line

Clipped Peak Saturation

Set Exposure Time

Exposure_time = 10.5 Pixel_clock = 30; Frame rate =

max

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Curve from 1 line

Color Calibration

Color projected by screen ≠ perceived by camera Generate Ramp Images in RGB

Red Ramp

Green Ramp Blue Ramp

Color Calibration

Project ramp images to mirror object

Red Ramp

Green Ramp Blue Ramp

Color Calibration

Response Curve for each ramp image

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RED Ramp Image

RED response curve : linear

Noisy response from other channels

Color Calibration

Find minimum and maximum values of perceived color values in each channel

MIN MAX

Red 17 132

Green 26 182

Blue 21 247

Color Calibration

Normalization of the linear Response Curve

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140Response Curve from Red Channel

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250Response Curve from Blue Channel

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300Normalized Response Curve - RED

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300Normalized Response Curve - GREEN

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300Normalized Response Cruve - BLUE

Geometrical Diagram

Mirror with various shift

• Vertical pattern with 1˚ angle increment• Obvious pattern shift observed

Θ = 0 Θ = 1

Θ = 3

Θ = 2

Θ = 4 Θ = 5

Shifted Color Curve

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250Theta = 0

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250Theta = 1

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250Theta = 2

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250Theta = 3

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300Theta = 4

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300Theta = 5

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300Theta = 6

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300Theta = 7

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300Theta = 8

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300Theta = 9

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300Theta = 10

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300Theta = 11

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250Theta = 12

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250Theta = 13

Theta Vs Shift

Shifted Color Curve

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250Theta = 0

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250Theta = 1

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250Theta = 2

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250Theta = 3

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300Theta = 4

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300Theta = 5

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300Theta = 6

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300Theta = 7

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300Theta = 8

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300Theta = 9

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300Theta = 10

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300Theta = 11

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250Theta = 12

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250Theta = 13

Θ Versus α

Angle

α

1 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

Angle – Shift Relationship

θ= 1o

α = 1.7 rad

δ= 97 pixels

Conclusion

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.

THANK YOU….

REFERENCES

  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