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Dynamic Refraction Stereo

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Title: No Slide Title Author: Kiriakos Kutulakos. Last modified by: Nigel Morris Created Date: 5/6/1997 3:41:10 PM Document presentation format: Custom – PowerPoint PPT presentation

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Title: Dynamic Refraction Stereo


1
Dynamic Refraction Stereo
Kiriakos N. Kutulakoskyros _at_ cs.toronto.edu
Nigel J. W. Morrisnmorris _at_ dgp.toronto.edu
7. Contributions
2. Related Work
  • Refractive disparity optimization gives stable
    reconstructions regardless of surface shape
  • Require no geometric assumptions other than
    single light refraction
  • Produces a full resolution height map and a
    separate, full-resolution normal map
  • Highly-detailed reconstructions for a variety of
    complex, deforming liquid surfaces
  • Single-view methods (require extra
    assumptions/optics)
  • Shape from Distortion (Murase, PAMI 1992) ?
    assumes constant mean distance
  • Shape from Refraction (Jähne et al., JOSA 1994)
    ? uses collimating lens lighting gradient
  • Multi-view methods
  • Sanderson et al. (PAMI 1998), Bonfort Sturm
    (ICCV 2003) ? static mirror scenes (known
    refractive index), ? optimization degrades
    for shallow liquid heights
  • Multi-media photogrammetry (Flach Maas, IAPRS
    2000) ? known parametric shape model

8. Experimental results
Simulations vs. Ground-truth Experiments
Refractive index estimation
q
Total reconstruction error as function of
refractive index (liquid was water)
Observed results (solid red) vs simulated results
(dotted blue) for 0.08 pixel localization error
for set of planar refractive surfaces at various
heights
Experiments with dynamic water surfaces
C(q)
Instantaneous 3D reconstructions
t1.32s
t0.6s
t0.92s
t0.92s
4. Refractive Stereo Algorithm
Height map
  • Discretize interval of possible refractive index
    values.For each value, do Steps 1-4
  • Step 1 Initialize correspondence function C(.)
    for time 0
  • Step 2 For each time t and each pixel q in
    Camera 1
  • 2a (Refractive disparity optimization)
    1D optimization along ray of q,
    searching for height hypothesis
    consistent with both viewpoints
  • 2b (Bundle adjustment) 5D optimization
    of p and n using reprojection error
  • Step 3 Fuse depth normal map to obtain 3D
    surface
  • Step 4 Update correspondence function for next
    frame
  • Choose refractive index value minimizing total
    reconstruction error (across all frames and
    pixels)

Tilt map
q
q
n
p
t0.4s
t0.3s
t0.35s
t0.3s
C(q)
C(q)
Height map
Tilt map
t0.91s
t0.75s
t0.83s
t0.91s
Height map
Tilt map
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