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paper by Yasutaka Furukawa, Jean Ponce. Presented in class by me. Apologized if any mistaken.
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Carved Visual Hulls for Image-Based ModelingYasutaka Furukawa, Jean Ponce
presented byPhongsathorn Eakamongul
Department of Computer ScienceAsian Institute of Technology
2009, July 27
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 1 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 2 / 49
Reference
silhouette image from http://www.flickr.com/photos/alexfrance/3693058077/
space carving, shadow curving, voxel coloring from Silvio Savarese’s slide.
http://www.cs.cornell.edu/courses/cs664/2005fa/Lectures/lecture15.pdf
graph cut from ECCV 2006 tutorial
Multi-view shape reconstruction, lecture from Dana Cobzas, PIMS Postdoc
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 3 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 4 / 49
Introduction
image-based Modeling
optimization
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 5 / 49
image-based Modeling
accuracy 1/200 ( 1 mm for 20 cm wide object ) from a set of low resolution(640x480px2) images∗
∗ From a comparison and evaluation of multi-view stereo reconstruction algorithms by ( Seitz et al. 2006 ), comparision of different method also available at
http://vision.middlebury.edu/mview/
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 6 / 49
image-based Modeling
choose a surface representation
define a photo-consistency function ( discrepancy between different projectionsof their surface points )
solve the following minimization
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 7 / 49
Representation
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 8 / 49
Volumetric representation
shape from silhouette ( Visual Hull )
space carving
voxel coloring
others i.e. Voxel-based, Image ray based, Axis-aligned, etc.
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 9 / 49
shape from silhouette ( Visual Hull )image object’s contour
Why contour ?
No texture
No shading
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 10 / 49
shape from silhouettevisual cones intersection
Carve all voxels outside the cone
Not photo-consistent (only to binary images)
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 11 / 49
Space carving algorithm
Initialize to a volume V containing the true scene
Choose a voxel on the current surface
Project to visible input images
Carve away voxels if not photo-consistent with images
Repeat until convergence
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 12 / 49
Voxel Coloring
Assign colors (RGBA : color + opacity) to voxels consistent with the input images.
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 13 / 49
Optimization
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 14 / 49
Graph cut
find the minimum cut , by compute the maximum flow , and look for the cut(s) thatseparate origin and destination by cutting through bottlenecks of the network.
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 15 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 16 / 49
Steps
shape from silhouette
initialize deformation of a surface mesh under photoconsistency constriantsoutput : rims that are used in graph cuts
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 17 / 49
Steps
global optimization process : use graph cuts with photoconsistency constraints +geometric contraints ( rims )
local refinement : enforce geometric contraints + photoconsistency constraints
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 18 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 19 / 49
Identify Rims
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 20 / 49
Identify Rimscone strips
( Lazebnik et al 2007 ) represent visual hull in terms of polyhedral cone stripsΓ : rim
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 21 / 49
Identify Rimscone strips
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 22 / 49
Identify Rimstriangulated mesh model
( Lazebnik et al 2007 ) algorithm to obtain triangulated mesh model
In practices, measurement errors result to multiple horizontal neighbour
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 23 / 49
Identify RimsImage Discrepancy
Since rim segments are only part that touch surface of object, they can be foundas strip curves that minimize some measure of image discrepancy
Image Discrepancy Score ( Faugeras and Keriven 1998 )
f (p) = 2τ(τ−1)
Pτi=1
Pτj=i+1 1− exp(− (1−NCC(hi ,hj ))2
2σ21
)
NCC(hi , hj ) : normalizated cross correlation between hi and hj
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 24 / 49
Identify Rims
image discrepancy is smallest values, so find shortest path ; path length = imagediscrepancy function
find shortest path by dynamic programming
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 25 / 49
Identify Rimsfind shortest path by dynamic programming
rim can be discontinous due to T-junction
First, Assume rim discontinuities occur only at right or left end points of eachconnected strip component
change from undirected graph to direct graph and apply dynamic programming
result :
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 26 / 49
Identify Rimsremove false rim segmentation
assumption break at complicated structure i.e. the fold of human clothremove false rim segmentation
Among all vertics identified as rim points, filter out false-positivesvertex v is detected as false positive if
either 4Rl < g(v) or Rl < g(v) [vertical size is too large] andf∗(v) < η [vertical size is not small enough] and [average NCC wrose than η]
average NCC score ( Faugeras and Keriven 1998 )
f ∗(p) = 2τ(τ−1)
Pτi=1
Pτj=i+1 NCC(hi , hj )
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 27 / 49
Identify Rims
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 28 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 29 / 49
Global Optimization
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 30 / 49
Global Optimization
use rim Γ to split surface Ω into Gi (i = 1, ..., k)
iteratively deform Gi inwards to generate multiple layers of 3D graph Ji andassociate photoconsistency weights to the edge of this graph
use graph cuts to carve surface
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 31 / 49
Global OptimizationMove Vertices down
At each iteration, move every vertex v along its surface normal N(v) and applysmoothing
v ← v − ελ
(ζ1f (v) + ζ2)N(v) + s(v)
notice that surface shrinks faster where image discrepancy function is largerConstant they use in all their experimentζ1 = 100, ζ2 = 0.1, β1 = 0.4, β2 = 0.3, λ = 20, ρ = 40, ε = 0.5 ∗ AverageLengthInGi
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 32 / 49
Global OptimizationBuilding a graph and Apply Graph Cuts
build vertical edgebuild horizontal edgeassign vertical and horizontal edge weight
weight of edge (vi , vj )
wij =α(f (vi )+f (vj )(δi +δj ))
d(vi ,vj )
f (vi ) : photoconsistency function; d(vi , vj ) : length of edge; δi : sparity of verticesaround vi , α = 1.0 for horizontal, 6.0 for vertical∞ edge weight for edge connected to source and sinkapply graph cuts
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 33 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 34 / 49
Local Refinement
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 35 / 49
Local Refinementlocal minimum
increase resolution of mesh ( Hoppe et al 1993 ) until image projections of edgesbecome approximiately 2 px in length
rim consistency ( Hernandex Esteban and Schmitt 2004 )
r(vk ) = ¯v∗j rjexp(− ¯vk rj− ¯v∗k rj )
2/2σ22P
v′k∈Vjexp(−( ¯vk′ rj− ¯v∗j rj )2/2σ22)
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 36 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 37 / 49
Result7 data sets
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 38 / 49
Result
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 39 / 49
ResultRim Identification Result
Filtering ratio : how many % of identified rim points has been filtered out as outliers ( for eachcontour )Sizes of components : show 3 largest connected components inside identified rim-segments
From table, visual hull boundary is mostly covered by a single large connected component exceptfor Twin data set, which has many input images, and hence, many rim curves.
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 40 / 49
ResultRunning Time (with 3.4 GHz Pentium 4)
bottleneck of computation is global optimization and local refinement step ( takes about2 hr )
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 41 / 49
ResultTesting algorithm w/o graph cuts phase ( use only local method )
local minimum problem
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 42 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 43 / 49
Comparisons
( This paper ) rim constriants( Voiazis et al 2005 ) add inflationary ballooning term to enery function in graph cuts toprevent over-carving
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 44 / 49
Comparisons
( Hernandex Exteban and Schmitt 2004 ) In local refinement, use gradient flow insteadof direct derivatives 5f (v)Some other differences, i.e. local iterative deformation, that have problem avoidinglocal minima.
mistake in rim-identification stepsoutperforms in reconstructingconcave structure
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 45 / 49
Comparisons
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 46 / 49
ComparisonsMulti-view stereo evaluation ( http://vision.middlebury. edu/mview/ )
Temple data set
Accurracy : distance d that bring 90% of result within ground-truth surfaceCompleteness : % ground-truth surface that lies within 1.25 mm of the result
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 47 / 49
Outline
1 Reference
2 Introduction
3 Steps
4 Identify Rims
5 Global Optimization
6 Local Refinement
7 Result
8 Comparison
9 Limitation & Future work
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 48 / 49
Limitation & Future work
Since, cannot handle concavities too deep to be carved by the graph cuts. i.e. eyesockets of skullsTo overcomes this, combine their works with sparse wide-baseline stereo frominterest point (e.g. Schaffalitzky and Zisserman 2001) in order to incorporatestronger geometric constraints in the carving and local refinement stages
handle non-Lambertain surfaces ( Soatto et al 2003 )
simutaneous camera calibration where both camera parameters and surfaceshape are refined simultaneously using bundle adjustment ( Uffenkamp 1993 )
Phongsathorn (AIT) Carved Visual Hulls for Image-Based Modeling Machine Vision Student Presentation 49 / 49