Post on 05-Jan-2016
description
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M15338: Depth Map Estimation
Software version 2
April, 27th 2008, Archamps
Olgierd StankiewiczKrzysztof Wegner
team supervisor: Marek DomańskiChair of Multimedia Telecommunications and Microelectronics
Poznan University of Technology, Poland
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Outline Depth map quality measurement
Ground-truth map View resynthesis
View synthesis tool Depth map estimation tools
Belief Propagation based estimation Accuracy refinement by mid-level
hypothesis Summary
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Depth map quality
Commonly used: ‘Bad-Pixels’
Miss information about error magnitude and energy
Requires ground-truth disparity map
thresholdyxdyxG
thresholdyxdyxGyxpixelbad
),(),(0
),(),(1),(
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Depth map quality NBP-SAD (Normalized Bad Pixel SAD)
NBP-SSD (Normalized Bad Pixel SSD)
Still, requires ground-truth disparity map
pixelsbadyx
yxdyxGpixelsbadofcount
SADNBP,
),(),(1
pixelsbadyx
yxdyxGpixelsbadofcount
SSDNBP,
2),(),(
1
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Depth map quality measurement by view resynthesis
End-user never sees depth-map Resynthesis
No standarized method Tool employs straight-forward method
PSNR (Peak Signal-to-Noise Ratio)of resynthesized view as quality measure
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Bad-Pixels vs PSNRBad-pixels vs PSNR of resynthesis
HSABM+OF [1]ThreeViewBP [9]
AdaptingBP [4]
SubPixDoubleBP [6]
AdaptOvrSegBP [7]
PlaneFitBP [8]
Our proposal BP
Double BP[5]
SSD+MF [3]
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30
31
32
33
34
35
36
0,00% 1,00% 2,00% 3,00% 4,00% 5,00% 6,00% 7,00% 8,00% 9,00%
Bad-pixels [%]
PS
NR
[d
B]
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View synthesis tool
Simple and straight-forward For linearly positioned stereo pairs
only Two disparity maps and
corresponding reference views Weighting of pixels from side-views,
translated according to their disparity
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View synthesis tool
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Belief Propagation based depth estimation tool
Alternative for Hierarchical-Shape Adaptive Block Matching
Employs message passing for optimization of disparity map
hierarchical processing in layers
Pixel differences (1-point SAD) used as observations
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Message passing in Belief Propagation
mt s→d – message passed in t-th iteration from node s to node d, V(fp,fq) – cost of belief change from disparity fp to disparity fq.
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Message in Belief Propagation
Single message contains information about all possible disparities
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Hierarchical processing in Belief Propagation
Higher resolution
Lower resolution
from the lowest resolution to the full resolution in
coarse-to-fine manner
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Belief propagation
Vpq(xp,xq) – transition cost in node q between disparity xp and xq
insisted by nodeł p
Vp(xp) – observation in node p about disparity xp (SAD value)mpq(xq) – message from node p to q about disparity xq
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Belief propagation
Pot model Simpleand computationally efficient .
Stable beliefs are prefered
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Belief propagation results
1th iteration 20 iterations 300 iterations
Middlebury test results – 1,65% of bad-pixelsBest Middlebury algorithm – 0,88% of bad-pixels
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Bad-Pixels vs PSNRBad-pixels vs PSNR of resynthesis
HSABM+OF [1]ThreeViewBP [9]
AdaptingBP [4]
SubPixDoubleBP [6]
AdaptOvrSegBP [7]
PlaneFitBP [8]
Our proposal BP
Double BP[5]
SSD+MF [3]
29
30
31
32
33
34
35
36
0,00% 1,00% 2,00% 3,00% 4,00% 5,00% 6,00% 7,00% 8,00% 9,00%
Bad-pixels [%]
PS
NR
[d
B]
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Accuracy refinement by mid-level hypothesis
Low computational cost Improves accuracy of disparity
map (number of disparity levels) Spatial resolution unchanged Focuses on unit-step edges in
disparity map
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Unit-step edges
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Mid-level hypothesis
Hypothesis spread along unit-step edges
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Refinement by mid-level hypothesis
Pixel accurate disparity (1x) After refinement (8x)
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Works over untextured regions
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ResultsChange of bad-pixels relative to (x1) during iterative refinement
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
120,00%
x1 x2 x4 x8 x16
Bad
-pix
els
chan
ge
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Summary
New version of experimental Depth Estimation software
Quality measurement problem with respect to multi-view applications
Simple view resynthesis tool Belief Propagation depth estimation
tool Novel technique for accuracy
refinement