3d industrial r econstruction by fitting csg models to a combination of images and point clouds
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3D industrial r econstruction by fitting CSG models to a combination of images and point clouds. Tahir Rabbani Frank van den Heuvel. Overview. Introduction The approach to modelling Model fitting: point clouds and images Fitting experiments: cylinder and box Conclusions. Introduction.

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3d industrial r econstruction by fitting csg models to a combination of images and point clouds

3D industrialreconstruction by fitting CSG models to a combination of images and point clouds

Tahir Rabbani

Frank van den Heuvel

overview
Overview
  • Introduction
  • The approach to modelling
  • Model fitting: point clouds and images
  • Fitting experiments: cylinder and box
  • Conclusions
introduction
Introduction
  • New approach for semi-automatic reverse engineering of industrial sites from images presented to ISPRS congress Amsterdam 2000
introduction4
Introduction
  • New approach for semi-automatic reverse engineering of industrial sites from images presented to ISPRS congress Amsterdam 2000
  • 2001: Automated 3D reconstruction of industrial installations from laser and image data
    • EU-project: Services and Training through Augmented Reality (STAR)
    • Partners: Siemens, KULeuven, EPFL, UNIGE, Realviz
modelling p ipeline overview
Modelling Pipeline Overview

1. Segmentation

Range data

Images

2. Object Recognition

CAD model

Database

User

Input

3. Constraint detection

4. Integrated adjustment

modelling p ipeline 1 4
Modelling Pipeline(1/4)
  • Segmentation
    • Grouping points of surface patches
modelling p ipeline 2 4
Modelling Pipeline(2/4)
  • Segmentation
    • Grouping points of surface patches
  • Object Recognition
    • Finding planes and cylinders
modelling p ipeline 3 4
Modelling Pipeline(3/4)
  • Segmentation
    • Grouping points of surface patches
  • Object Recognition
    • Finding planes and cylinders
  • Constraint detection
    • Completing the CSG model
constructive solid geometry csg
Constructive Solid Geometry (CSG)
  • CSG-tree:
    • primitives (box, cylinder, …)
    • shape, pose parameters
    • Constraints on parameters
modelling p ipeline 4 4
Modelling Pipeline(4/4)
  • Segmentation
    • Grouping points of surface patches
  • Object Recognition
    • Finding planes and cylinders
  • Constraint detection
    • Completing the CSG model
  • Integrated adjustment
    • Fitting of CSG model to range data and imagery
integrated adjustment
Integrated adjustment
  • Least-squares adjustment integrating:
    • Fitting of CSG model to point cloud
    • Fitting of projected model to images
    • Geometric constraints on (relative) pose and shape
fitting to a point cloud
Fitting to a point cloud
  • Minimise sum of squared distances in object space

= shortest distance of a given point

from

= model surface defined by

= 3D laser points

fitting to images
Fitting to images
  • Minimise sum of squared distances in image space

= shortest distance of a given point

from

= back projected CSG model with parameters

= 2D image points

fitting solution
Fitting – Solution
  • Levenberg-Marquardt method:
    • Improved convergence compared to least-squares
    • Identical to least-squares for λ=0
  • Least-squares adjustment:
    • Assessment of precision through error propagation
    • Analysis of quality of fit (residuals)
experiments
Experiments
  • Cylinder fitting:
    • 6179 laser points Cyrax2500 (σ=5mm)
    • 3 images, 383 points Calibrated Coolpix5000 (σ=1pixel)
  • Box fitting:
    • 25162 laser points
    • 3 images, 416 points
experiment cylinder fitting results
Experiment: cylinder fitting results
  • Standard deviations

Parameter Laser Integrated

Length (mm) 1.86

Radius (mm) 0.63 0.57

X (mm) 0.84 0.76

Y (mm) 1.34 0.86

Z (mm) 119.90 1.65

t0 2.3e-3 2.0e-3

t1 4.0e-3 2.9e-3

t2 3.6e-1 2.4e-1

experiment cylinder fitting results17
Standard deviations:Experiment: cylinder fitting results
  • Conclusions:
    • Only laser: length and position in Z undetermined
    • Integrated: better than 2mm, similar results for using only 1 or 2 images

Parameter Laser Integrated

Length (mm) 1.86

Radius (mm) 0.63 0.57

X (mm) 0.84 0.76

Y (mm) 1.34 0.86

Z (mm) 119.90 1.65

t0 2.3e-3 2.0e-3

t1 4.0e-3 2.9e-3

t2 3.6e-1 2.4e-1

experiment box fitting results
Experiment: box fitting results
  • Standard deviations

Parameter Laser Integrated

X size (mm) 2.89 0.66

Y size (mm) 0.53

Z size (mm) 0.32

X (mm) 3.08 0.65

Y (mm) 0.55 0.16

Z (mm) 389.84 0.31

q0 4.0e-2 3.0e-2

q1 2.4e-4 1.0e-4

q2 5.2e-4 5.0e-5

q3 3.4e-4 2.0e-4

experiment box fitting results20
Experiment: box fitting results
  • Standard deviations
  • Conclusions:
    • Only laser: sizes in Y and Z undetermined
    • Size in X highly correlated with position in X
    • Position in Z undetermined
    • Integrated: better than 1mm, similar results for using only 1 or 2 images

Parameter Laser Integrated

X size (mm) 2.89 0.66

Y size (mm) 0.53

Z size (mm) 0.32

X (mm) 3.08 0.65

Y (mm) 0.55 0.16

Z (mm) 389.84 0.31

q0 4.0e-2 3.0e-2

q1 2.4e-4 1.0e-4

q2 5.2e-4 5.0e-5

q3 3.4e-4 2.0e-4

why fusion of laser and image data
Why fusion of laser and image data?
  • Instrumentation developments
    • Scanners with integrated high-resolution camera
  • Accuracy improvement
    • Complementary: Laser for surfaces, image for edges
  • Flexibility of image acquisition: Completeness
  • Non-geometric information (What is there?)
future conclusions
Future / Conclusions
  • Future research:
    • Estimation of exterior orientation parameters
    • Integration of geometric constraints
  • Conclusions:
    • Integrated adjustment of laser and image data developed and analysed
future conclusions24
Future / Conclusions
  • Future research:
    • Estimation of exterior orientation parameters
    • Integration of geometric constraints
  • Conclusions:
    • Integrated adjustment of laser and image data developed and analysed
    • Laser and image data are complementary:

Keep the best of both worlds !

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