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Hardware-Accelerated Silhouette Matching. Hendrik Lensch, Wolfgang Heidrich, and Hans-Peter Seidel Max-Planck-Institut f ür Informatik, Saarbrücken (Germany). Overview. Motivation Comparing Silhouettes Stitching and Combining Textures Results and Conclusions. Geometry 3D scanner.

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hardware accelerated silhouette matching

Hardware-AcceleratedSilhouette Matching

Hendrik Lensch, Wolfgang Heidrich,

and Hans-Peter Seidel

Max-Planck-Institut für Informatik,

Saarbrücken (Germany)

overview
Overview
  • Motivation
  • Comparing Silhouettes
  • Stitching and Combining Textures
  • Results and Conclusions
acquiring real world models
Geometry

3D scanner

Texture data

digital camera

Acquiring Real World Models

single sensor vs. multiple sensors

3d 2d registration
3D – 2D Registration
  • Find the camera setting for each 2D image.
camera model
Camera Model
  • Transformations
    • to camera coordinates (extrinsic):
    • to 2D image space (intrinsic):

 determine R, t and f (6+1 dimensions)

similarity measure
Similarity Measure
  • Which features to investigate?
    • no color information on the model
    • correspondence of geometric features hard to find
similarity measure7
Similarity Measure
  • Compare silhouettes [Etienne de Silhouette 1709-1767]
    • model: render monochrome
    • photo: automatic histogram-based segmentation
similarity measure8
Similarity Measure
  • Compare silhouettes [Etienne de Silhouette 1709-1767]
    • model: render monochrome
    • photo: automatic histogram-based segmentation
distance measure for silhouettes
Point-to-outline distances

slow because points on the outline must be determined

speedup by distance maps

Distance Measure for Silhouettes
pixel based distance measure
Count the number of pixels covered by just one silhouette.

XOR the images

compute histogram (hardware)

gives linear response to the displacement

intensity

x

0

difference

1

x

0

displacement

Pixel-based Distance Measure

1

pixel based distance measure11
Count the number of pixels covered by just one silhouette.

XOR the images

compute histogram (hardware)

gives linear response to the displacement

intensity

1

1

x

0

difference

1

x

0

Pixel-based Distance Measure

displacement

approximation of squared distances
Use smooth transitions

blur images

integrate squared differences

faster convergence

reduced variance

higher evaluation cost

Approximation ofSquared Distances
approximation of squared distances13
Use smooth transitions

blur images

integrate squared differences

faster convergence

reduced variance

higher evaluation cost

filtersize

intensity

intensity

1

1

1

x

x

0

0

difference

1

1

x

0

0

Approximation ofSquared Distances

1

difference

x

non linear optimization
Non-linear Optimization
  • Downhill Simplex Method [Press 1992]
    • works for N dimensions
    • no derivatives
    • easy to control
simplex method in 3d
Simplex Method in 3D

original simplex

reflection and/or

expansion

shrinking

random

perturbation

hierarchical optimization
Hierarchical Optimization
  • optimize on low resolution first
  • restart optimization to avoid local minima
  • switch to higher resolution
  • mesh resolution can be adapted
starting point generation
Starting Point Generation
  • set camera distance tz depending on object size
  • settx and ty to zero
  • select 48 sample rotations
  • run optimization for each of the samples

(40 evaluations)

  • select top 5 results
  • restart optimization (200 evaluations)
  • take best result as starting point
texture stitching
Texture Stitching
  • projective texture mapping
  • assign one image to each triangle
    • triangle visible in image? (test every vertex)
    • select best viewing angle
    • discard data near depth discontinuities
blending across assignment borders
find border vertices

release all triangles around them

assign boundary vertices to best region

assign alpha-values for each region

1 to vertices included in the region

0 to all others.

Blending Across Assignment Borders
results and conclusions
Results and Conclusions
  • Problems solved:
    • automatic texture registration (R, t, f)
    • view-independent texture stitching
    • blending across assignment boundaries
    • rough manual alignment helps (speedup, failures)
  • Further problems:
    • extract purely diffuse part of texture
    • generate texture where data is missing
questions
Questions?
  • visit us at
  • www.mpi-sb.mpg.de