supermatching : feature matching using supersymmetric geometric constraints
DESCRIPTION
SuperMatching : Feature Matching using Supersymmetric Geometric Constraints. Submission ID: 0208. Overview. SuperMatching is: A fundamental matching algorithm in GRAPH ics and VISION tasks. Overview. SuperMatching is: - PowerPoint PPT PresentationTRANSCRIPT
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SuperMatching: Feature Matching using
Supersymmetric Geometric Constraints
Submission ID: 0208
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Overview• SuperMatching is:
– A fundamental matching algorithm in GRAPHics and VISION tasks
![Page 3: SuperMatching : Feature Matching using Supersymmetric Geometric Constraints](https://reader035.vdocuments.us/reader035/viewer/2022062501/56816726550346895ddbb8bd/html5/thumbnails/3.jpg)
Overview
Pairwise matching using uniformly sampled points on the 3D shapes
• SuperMatching is:– A fundamental matching algorithm in GRAPHics and VISION tasks
![Page 4: SuperMatching : Feature Matching using Supersymmetric Geometric Constraints](https://reader035.vdocuments.us/reader035/viewer/2022062501/56816726550346895ddbb8bd/html5/thumbnails/4.jpg)
Overview• SuperMatching is:
– Using feature tuples (triangles or higher-order tuples)– Formulated as a supersymmetric higher-order affinity tensor
![Page 5: SuperMatching : Feature Matching using Supersymmetric Geometric Constraints](https://reader035.vdocuments.us/reader035/viewer/2022062501/56816726550346895ddbb8bd/html5/thumbnails/5.jpg)
Overview• SuperMatching is:
– Using feature tuples (triangles or higher-order tuples)– Formulated as a supersymmetric higher-order affinity tensor
Third-order diagram (edge length invariance in 3D triangles)
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3D rigid shapes scans
Initial poses Matching result
I II
IIIII
• Pairwise matching of Rooster scans
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3D rigid shapes scans
Initial poses Matching result
I II
IIIII
• Pairwise matching of Rooster scans
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3D rigid shapes scans• Comparison with 4PCS [Aiger et al. 2008]
[Aiger et al. 2008]SuperMatching
Rooster II-III pairwise registration
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3D rigid shapes scans• Comparison with 4PCS [Aiger et al. 2008]
[Aiger et al. 2008]SuperMatching
Rooster II-III pairwise registration
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3D real depth scans• Colored Scene captured by Kinect
Source shape
Target shape
Final alignment Pairwise Matching
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3D real depth scans• Colored Scene captured by Kinect
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3D articulated shapes• Articulated Robot between frame 9 and 10
[Chang and Zwicker 2009]SuperMatching
distortion
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3D articulated shapes• Articulated Robot between frame 9 and 10
[Chang and Zwicker 2009]SuperMatching
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Deformable surfaces
Spectral method[Cour et al. 2006]
Hypergraph matching [Zass and Shashua 2008]
A third-order tensor[Duchenne et al. 2009]
SuperMatching
cloth: F80-F90 cushion: F144-F156
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Deformable surfaces• Accuracy and Time-costs
Dataset cloth cushion
PairwiseMatching
F80-F90
F90-F95
F95-F100
F100-F105
F144-F156
F156-F165
F165-F172
F172-F188
Times(Sec)
Super-Matching 83% 85% 84% 81% 66% 60% 69% 56% 8
[Zass and Shahua 2008] 73% 79% 70% 72% 44% 39% 54% 43% 6.5
[Duchenne et al. 2009] 67% 77% 73% 65% 39% 31% 47% 42% 13
[Cour et al. 2006] 27% 29% 22% 27% 14% 5% 28% 7% 5
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Deformable surfaces• Accuracy and Time-costs
Dataset cloth cushion
PairwiseMatching
F80-F90
F90-F95
F95-F100
F100-F105
F144-F156
F156-F165
F165-F172
F172-F188
Times(Sec)
Super-Matching 83% 85% 84% 81% 66% 60% 69% 56% 8
[Zass and Shahua 2008] 73% 79% 70% 72% 44% 39% 54% 43% 6.5
[Duchenne et al. 2009] 67% 77% 73% 65% 39% 31% 47% 42% 13
[Cour et al. 2006] 27% 29% 22% 27% 14% 5% 28% 7% 5
More accurate with competitive time
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Deformable surfaces• Accuracy and Time-costs
Dataset cloth cushion
PairwiseMatching
F80-F90
F90-F95
F95-F100
F100-F105
F144-F156
F156-F165
F165-F172
F172-F188
Times(Sec)
Super-Matching 83% 85% 84% 81% 66% 60% 69% 56% 8[Zass and Shahua 2008] 73% 79% 70% 72% 44% 39% 54% 43% 6.5
[Duchenne et al. 2009] 67% 77% 73% 65% 39% 31% 47% 42% 13
[Cour et al. 2006] 27% 29% 22% 27% 14% 5% 28% 7% 5
More accurate with competitive time
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Thanks
Real 3D data captured by Kinect
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Thanks
Real 3D data captured by Kinect
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