Robot vision lesson 1a structured light 3d reconstruction matthias r ther christian reinbacher
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ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher PowerPoint PPT Presentation


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ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher. Structured Light Methods. Goal: Robust 3D Reconstruction through triangulation Project artificial pattern on the object Pattern alleviates the correspondence problem Variants:

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ROBOT VISION Lesson 1a: Structured Light 3D Reconstruction Matthias Rüther, Christian Reinbacher

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Robot vision lesson 1a structured light 3d reconstruction matthias r ther christian reinbacher

ROBOT VISIONLesson 1a: Structured Light 3D ReconstructionMatthias Rüther, Christian Reinbacher


Structured light methods

Structured Light Methods

  • Goal: Robust 3D Reconstruction through triangulation

  • Project artificial pattern on the object

  • Pattern alleviates the correspondence problem

  • Variants:

    • Laser Pattern (point, line)

    • Structured projector pattern (several lines, pattern sequence)

    • Random projector pattern


Structured light range finder

Structured Light Range Finder

1. Sender (projects plane)

2. Receiver (CCD Camera)

Sensor image

Geometry Z- direction

X- direction


Robot vision lesson 1a structured light 3d reconstruction matthias r ther christian reinbacher

1 plane -> 1 object profile

  • To get a 3D profile:

    • Move the object

    • Scanning Unit for projected plane

    • Move the Sensor

Object motion by conveyor band:

=> synchronization: measure distance along conveyor

=> y-accuracy determined by distance measurement

Scanning Units (e.g.: rotating mirror) are rare (accurate measurement of mirror motion is hard, small inaccuracy there -> large inaccuracy in geometry

Move the sensor: e.g. railways: sensor in wagon coupled to speed measurement


Commercially available

Commercially Available

Person Scanners

Cultural Heritage

Rapid Prototyping


Problems of laser profile

Problems of Laser Profile

  • Occlusions:

    Object points need to be seen from Laser and Camera viewpoint

  • Sharpness and Contrast:

    Both camera and laser need to be in focus

  • Speckle noise:

    Laser always shows “speckle noise”, caused by interference of coherent light.

    -> where is the center of the stripe?


Multiple sheets of light

Multiple Sheets of Light

Project multiple Laser planes simultaneously to reduce measurement time.

Problem:

Separation of stripes in the image

Application:

Smoothness check of flat surfaces


Robot vision lesson 1a structured light 3d reconstruction matthias r ther christian reinbacher

Pattern projection

Range Image

Projected light stripes

  • Camera: IMAG CCD, Res:750x590, f:16 mm

  • Projector: Liquid Crystal Display (LCD 640), f: 200mm, Distance to object plane: 120cm


Projector

Projector

Lamp

Lens system

LCD - Shutter

Pattern structure

Line projector (e.g.: LCD-640)

Focusing lens (e.g.: 150mm)

Example


Depth decoding

Depth decoding

Project Temporal sequenceof n binarymasks. Ateachpixel, the temporal sequenceofintensities (I1, …, In) gives a binarynumberwhichdenotedthecorrespondingprojectorcolumn.

Project  Acquire  Decode  Triangulate


Coded light phase shift

Coded Light + Phase Shift

Binary code is limited to pixel accuracy (or less).

Increase accuracy to sub-pixel by projecting sine wave after code and measuring phase shift between projected and captured pattern. Decode phase from four samples of sine period, shifted by pi/2.


Coded light phase shift1

Coded Light + Phase Shift

Increase accuracy to sub-pixel by projecting sine wave after code and measuring phase shift between projected and captured pattern. Decode phase from four samples of sine period, shifted by pi/2.

code

Image column (x)

phase

+

2

0

Image column (x)


Other coding methods possible

Other Coding Methods Possible

Joaquim Salvi,

Pattern codification strategies in structured light systems


The kinect working principle

The Kinect Working Principle

  • Triangulation based depth sensor

  • Static pattern projection

  • Heavy exploitation of redundancy

  • Extremely robust/conservative depth maps


The sensor system

The Sensor System

IR Lens:

F~6mm FOV~55°

Diffractive Optical

Element (DOE)

Laser

830nm, 60mW

class 3B without optics, 1 with optics,

no amplitude modulation

IR Bandpass

RGB Lens:

F~2.9mm, FOV~65°

IR Camera:

CMOS, rolling shutter, 1.3MP, ½“, 10bit

RGB Camera:

CMOS, rolling shutter, 1.3MP, 1/4“, 10bit

Peltier Element

Temperature Stabilization

Stereo Processor

Microphone Array

Accelerometer

Tilt Axis


The sensor system1

The Sensor System

  • Tx ~75mm

  • DOF 0.5m – 8m

  • FOV ~55°

  • Res. 640x480 (at most)

  • Internal max 1280x1024

Stereo Processor

Microphone Array

Accelerometer

Tilt Axis


The projection pattern

The Projection Pattern

IR Laser and Diffractive Optical Element create interference pattern

Pattern is static and identical for all Kinects


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