a fast local descriptor for dense matching engin tola, vincent lepetit, pascal fua computer vision...
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A Fast Local Descriptor for Dense Matching
Engin Tola, Vincent Lepetit, Pascal Fua
Computer Vision LaboratoryEPFL
2008-06-10
MotivationNarrow baseline : Pixel Difference + Graph Cuts*
groundtruth
pixel difference
input frame
input frame
* Y. Boykov et al. Fast Approximate Energy Minimization via Graph Cuts. PAMI’01.
MotivationWide baseline : Pixel Difference + Graph Cuts
groundtruth
USE A DESCRIPTOR
input frame
input frame
pixel difference
MotivationWide baseline : SIFT Descriptor*+ Graph Cuts
groundtruth
SIFT
250 Seconds
* D. Lowe. Distinctive Image Features from Scale-Invariant Keypoints. IJCV’04
input frame
input frame
MotivationWide baseline : DAISY Descriptor+ Graph Cuts
groundtruth
DAISY
5 Seconds
input frame
input frame
MotivationHistogram Based Descriptors: SIFT, GLOH, SURF…
- Perspective robustness- Proven good performance- Robustness to many image transformations
Cons
- No efficient implementation exists for dense computation- Do not consider occlusions
Design a descriptor that is as robust as SIFT or GLOH but can be computed much more effectively and handle occlusions.
Problem Definition
epipolar lineepipolar
line
Virtual Camera
Input Frames
descriptor
Histogram based Descriptors…SIFT Computation
…
Histogram based Descriptors…SIFT Computation
SIFT -> DAISY
SIFT
+ Good Performance- Not suitable for
dense computation
SIFT -> DAISY
SIFT Sym.SIFT
+ Gaussian Kernels : Suitable for Dense Computation
GLOH*
+ Good Performance+ Better Localization- Not suitable for
dense computation
+ Good Performance- Not suitable for
dense computation
* K. Mikolajczyk and C. Schmid. A Performance Evaluation of Local Descriptors. PAMI’04.
SIFT -> DAISY
DAISY
+ Suitable for dense computation + Improved performance:*
+ Precise localization+ Rotational Robustness
Sym.SIFT
+ Suitable for Dense Computation
GLOH
+ Good Performance+ Better Localization- Not suitable for
dense computation
* S. Winder and M. Brown. Learning Local Image Descriptors in CVPR’07
DAISY Computation
…
…
…
DAISY Computation
…
…
…
DAISY Computation DAISY : 5sSIFT : 250s
- Rotating the descriptor only involves reordering the histograms. - The computation mostly involves 1D convolutions, which is fast.
Depth Map Estimation
x
NN OZpOZxDpDOZp ),(),|)(()|,( :1:1
Descriptors
Occlusion
Depthmap Evidence Smoothness Prior
Occlusions should be handled explicitly!
Depth Map Estimation
OZxMpxMOZxDpOZxDp mm
mNN ,|)()(,,|)(,|)( :1:1
Evidence
P. of a specific Occlusion Mask
Occlusion Masks
Depth Map Estimation
OZxMpxMOZxDpOZxDp mm
mNN ,|)()(,,|)(,|)( :1:1
Evidence
Occlusion Masks
P. of a specific Occlusion Mask
Experiments
DAISY SIFT
SURF NCCPixel Diff
Laser Scan
Comparing against other Descriptors
100
90
80
70
60
50
40
30
20
10
0
Correct Depth % for Image Pairs
ExperimentsComparison with other Descriptors
DAISY
SIFT
SURF
NCC
PIXEL
100
90
80
70
60
50
40
30
20
10
0
Correct Depth % for Image Pairs
ExperimentsComparison with other Descriptors
DAISY
SIFT
SURF
NCC
PIXEL
Correct Depth % vs Error Threshold
Herz-Jesu Sequence
87.4 % 83.9 % 83.8 %
84.9 % 91.8 % 91.8 %
90.8 %83.2 % 93.5 %
89.4 %80.2 % 90.7 %
Truly Occluded
Missed Depths
Missed Occlusions
Herz-Jesu Sequence
Ground TruthDAISY
Comparison with Strecha’05
Strecha’05: Wide baseline stereo from Multiple Views: A probabilistic Account
Strecha: 3072x2048
Comparison with Strecha’05
Strecha’05: Wide baseline stereo from Multiple Views: A probabilistic Account
768x512
Image TransformsContrast Change
Scale
Blurry Webcam Images
SIFTNCC
Image TransformsContrast Change
Scale
Blurry Webcam Images
DAISYNCC
Conclusion
DAISY:• Efficient descriptor for dense wide baseline matching.• Handles occlusions correctly. • Robust to perspective distortions.• Robust to lighting changes. • Can handle low quality imagery.
Future work:• Image-based rendering from widely spaced cameras. • Object detection and recognition.
DAISY Source Codehttp://cvlab.epfl.ch/software
Stereo Data and Ground Truthhttp://cvlab.epfl.ch/data
C. Strecha et al. On Benchmarking Camera Calibration and Multi-View Stereo for High Resolution Imagery, CVPR’08
Source Code & Data
Questions
DAISY Source Codehttp://cvlab.epfl.ch/software
Imageshttp://cvlab.epfl.ch/data
http://cvlab.epfl.ch/~tolaEngin Tola
DAISY Source Codehttp://cvlab.epfl.ch/software
Imageshttp://cvlab.epfl.ch/data
http://cvlab.epfl.ch/~tolaEngin Tola
QUESTIONS ?
Parameter Selection
THQ
=2TH
Q=4
R: 5->30R: 5->30
R: 5->30
THQ
=8
HQ=2 HQ=4 HQ=8
RQ:2->5 RQ:2->5 RQ:2->5
R: 5->30R: 5->30
R: 5->30
THQ
=2TH
Q=4
THQ
=8
HQ=2 HQ=4 HQ=8
RQ:2->5 RQ:2->5 RQ:2->5
Parameter Selection
R: 5->30R: 5->30
R: 5->30
THQ
=2TH
Q=4
THQ
=8
HQ=2 HQ=4 HQ=8
RQ:2->5 RQ:2->5 RQ:2->5
Wide BaselineNarrow Baseline
Max: 87 %> 86 %
V:328R=15, RQ=5,
THQ=8, HQ=8
V:52R=10, RQ=3,
THQ=4, HQ=4
V:104R=10, RQ=3,
THQ=4, HQ=8Max: 78%
V:328R=15, RQ=5,
THQ=8, HQ=8
V:200R=15, RQ=3,
THQ=8, HQ=8
V:104R=10, RQ=3,
THQ=4, HQ=8
> 77%
Parameter SelectionWide BaselineNarrow Baseline
R: 5->30R: 5->30
R: 5->30
TQ=2
TQ=4
TQ=8
Q:1->5 Q:1->5 Q:1->5
H=2 H=4 H=8
R: 5->30R: 5->30
R: 5->30
TQ=2
TQ=4
TQ=8
Q:1->5 Q:1->5 Q:1->5
H=2 H=4 H=8
0
100