multi-view stereo via volumetric graph-cuts george vogiatzis roberto cipolla cambridge univ....
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Multi-view Stereo via Multi-view Stereo via Volumetric Graph-cutsVolumetric Graph-cuts
George Vogiatzis
Roberto Cipolla
Cambridge Univ. Engineering Dept.
Philip H. S. Torr
Department of ComputingOxford Brookes University
Multi-view Dense StereoMulti-view Dense Stereo
Calibrated images of Lambertian scene
3D model of scene
Multi-view Dense StereoMulti-view Dense Stereo
Volumetric
• Two main approaches• Volumetric• Disparity (depth) map
Dense Stereo Dense Stereo reconstruction problem:reconstruction problem:
Disparity-map
• Two main approaches• Volumetric• Disparity (depth) map
Shape representation Shape representation
• Disparity-maps• MRF formulation – good optimisation
techniques exist (Graph-cuts, Loopy BP)• MRF smoothness is viewpoint dependent• Disparity is unique per pixel – only
functions represented
Shape representation Shape representation
• Volumetric – e.g. Level-sets, Space carving etc.• Able to cope with non-functions• Levelsets: Local optimization• Space carving: no simple way to
impose surface smoothness
Our approachOur approach
• Cast volumetric methods in MRF framework
• Use approximate surface containing the real scene surface• E.g. visual hull
• Benefits:• General surfaces can be represented• No depth map merging required• Optimisation is tractable (MRF solvers)• Smoothness is viewpoint independent
Volumetric Graph cuts for Volumetric Graph cuts for segmentationsegmentation
• Volume is discretized • A binary MRF is defined on the voxels • Voxels are labelled as OBJECT and
BACKGROUND• Labelling cost set by OBJECT / BACKGROUND
intensity statistics• Compatibility cost set by intensity gradient
Boykov and Jolly ICCV 2001
Volumetric Graph cuts for Volumetric Graph cuts for stereostereo
Challenges:
• What do the two labels represent• How to define cost of setting them
• How to deal with occlusion• Interactions between distant voxels
Volumetric Graph cutsVolumetric Graph cuts
(x)
1. Outer surface
2. Inner surface (at constant offset)
3. Discretize middle volume
4. Assign photoconsistency cost to voxels
Volumetric Graph cutsVolumetric Graph cuts
Source
Sink
Volumetric Graph cutsVolumetric Graph cuts
Source
Sink
Cost of a cut (x)
dS
S
S
cut 3D Surface S
[Boykov and Kolmogorov ICCV 2001]
Volumetric Graph cutsVolumetric Graph cuts
Source
Sink
Minimum cut Minimal 3D Surface under photo-consistency metric
[Boykov and Kolmogorov ICCV 2001]
Photo-consistencyPhoto-consistency
• Occlusion
1. Get nearest point on outer surface
2. Use outer surface for occlusions
2. Discard occluded views
Photo-consistencyPhoto-consistency
• Occlusion
Self occlusion
Photo-consistencyPhoto-consistency
• Occlusion
Self occlusion
Photo-consistencyPhoto-consistency
• Occlusion
N
threshold on angle between normal and viewing direction
threshold= ~60
Photo-consistencyPhoto-consistency
• ScoreNormalised cross correlation
Use all remaining cameras pair wise
Average all NCC scores
Photo-consistencyPhoto-consistency
• ScoreAverage NCC = C
Voxel score
= 1 - exp( -tan2[(C-1)/4] / 2 )
0 1
= 0.05 in all experiments
ExampleExample
Example - Visual Hull Example - Visual Hull
Example - SliceExample - Slice
Example - Slice with graphcutExample - Slice with graphcut
Example – 3DExample – 3D
Protrusion problemProtrusion problem
• ‘Balooning’ force• favouring bigger volumes that fill the visual hull
L.D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-d and 3-d images. PAMI, 15(11):1131–1147, November 1993.
Protrusion problemProtrusion problem
• ‘Balooning’ force• favouring bigger volumes that fill the visual hull
L.D. Cohen and I. Cohen. Finite-element methods for active contour models and balloons for 2-d and 3-d images. PAMI, 15(11):1131–1147, November 1993.
(x) dS -
dV
S V
Protrusion problemProtrusion problem
Protrusion problemProtrusion problem
wij
SOURCE
wb
wb
GraphGraph
h
ji
wb = h3
wij = 4/3h2 * (i+j)/2[Boykov and Kolmogorov ICCV 2001]
ResultsResults
• Model House
ResultsResults
• Model House – Visual Hull
ResultsResults
• Model House
ResultsResults• Stone carving
ResultsResults
• Haniwa
SummarySummary
• Novel formulation for multiview stereo
• Volumetric scene representation
• Computationally tractable global optimisation using Graph-cuts.
• Visual hull for occlusions and geometric constraint
• Occlusions approximately modelled
Questions ?