Perspective Structure from Motion - PowerPoint PPT Presentation

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Perspective Structure from Motion

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With full perspective, can recover more information (translation ... r = reflectance (albedo) of surface. l = direction to light source. n = surface normal ... – PowerPoint PPT presentation

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Title: Perspective Structure from Motion


1
PerspectiveStructure from Motion

2
Orthographic ? Perspective
  • Last time, considered weak perspective
  • With full perspective, can recover more
    information (translation along optical axis)
  • Result can recover geometry and full motion up
    to global scale factor

3
Perspective SFM Methods
  • Bundle adjustment (full nonlinear minimization)
  • Methods based on factorization
  • Methods based on fundamental matrices
  • Methods based on vanishing points

4
Motion Field for Camera Motion
  • Translation
  • Motion field lines converge (possibly at ?)

5
Motion Field for Camera Motion
  • Rotation
  • Motion field lines do not converge

6
Motion Field for Camera Motion
  • Combined rotation and translationmotion field
    lines have component that converges, and
    component that does not
  • Algorithms can look for vanishing point,then
    determine component of motion around this point
  • Focus of expansion / contraction
  • Instantaneous epipole

7
SFM Algorithm
  • Compute optical flow
  • Find vanishing point (least squares solution)
  • Find direction of translation from epipole
  • Find perpendicular component of motion
  • Find velocity, axis of rotation
  • Find depths of points (up to global scale)

8
Shape from Shading and Texture

9
Lambertian Reflectance Model
  • Diffuse surfaces appear equally bright from all
    directions
  • For point illumination, brightness proportional
    to cos q

10
Lambertian Reflectance Model
  • Therefore, for a constant-colored object with
    distant illumination, can write E L r l?nE
    observed brightnessL brightness of light
    sourcer reflectance (albedo) of surfacel
    direction to light sourcen surface normal

11
Shape from Shading
  • The above equation contains some information
    about shape, and in some cases is enough to
    recover shape completely(in theory) if Lr and l
    are known
  • Similar to integration (surface normal is like a
    derivative), but only know a part of derivative
  • Have to assume surface continuity
  • No solution in the presence of noise

12
Variational Shape from Shading
  • Approach energy minimization
  • Given observed E(x,y), find shape z(x,y)that
    minimizes energywhere

13
Variational Shape from Shading
  • Solve by techniques from calculus of variations
  • Use Euler-Lagrange equations to get a PDE, solve
    numerically
  • Unlike with snakes, greedy methods tend not to
    work

14
Difficulties with Shape from Shading
  • How to find L, r, l?
  • Estimate based on scene statistics
  • Shadows
  • Non-Lambertian (e.g., specular) surfaces
  • More than 1 light, or diffuse illumination
  • Interreflections

15
Shape from Shading Results
Trucco Verri
16
Shape from Shading Results
17
Active Shape from Shading
  • Idea several (user-controlled) light sources
  • More data
  • Allows determining surface normal directly
  • Allows spatially-varying reflectance
  • Redundant measurements discard shadows and
    specular highlights
  • Often called photometric stereo

18
Photometric Stereo Setup
Rushmeier et al., 1997
19
Photometric Stereo Math
  • For each point p, can write
  • Constant a incorporates light source brightness,
    camera sensitivity, etc.

20
Photometric Stereo Math
  • Solving above equation gives (r /a) n
  • n must be unit-length ? uniquely determined
  • Determine r up to global constant
  • With more than 3 light sources
  • Discard highest and lowest measurements
  • If still more, solve by least squares

21
Photometric Stereo Results
Recovered normals (re-lit)
Inputimages
Recovered color
Rushmeier et al., 1997
22
Texture
  • Texture repeated pattern on a surface
  • Elements (textons) either identical or come
    from some statistical distribution
  • Shape from texture comes from looking at
    deformation of individual textons or from
    distribution of textons on a surface

23
Shape from Texture
  • Much the same as shape from shading, but have
    more information
  • Foreshortening gives surface normal (not just
    one component, as in shape from shading)
  • Perspective distortion gives information about
    depth directly
  • Sparse depth information (only at textons)
  • About the same as shape from shading, because of
    smoothness term in energy eqn.

24
Shape from Texture Results
Forsyth
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