global optimization methods for computer...
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Daniel CremersDepartment of Computer Science
University of Bonn
Global Optimization Methodsfor Computer Vision
Bonn Vision Workshop, October 8, 2009
2Global Optimization Methods for Computer VisionDaniel Cremers
Motion analysis3D reconstruction
Shape analysis Segmentation & tracking
Overview
3Global Optimization Methods for Computer VisionDaniel Cremers
Motion analysis3D reconstruction
Shape analysis Segmentation & tracking
Overview
4Global Optimization Methods for Computer VisionDaniel Cremers
Multiple View Reconstruction
5Global Optimization Methods for Computer VisionDaniel Cremers
Photoconsistency function:
Determine a surface of optimal photoconsistency by minimizing
Kolev, Klodt, Brox, Cremers, Int. J. of Computer Vision ’09:Theorem: Globally optimal surfaces can be computed via convex relaxation.
Multiple View Reconstruction
6Global Optimization Methods for Computer VisionDaniel Cremers
Non-convex energy
Non-convex versus Convex Energies
Convex energy
7Global Optimization Methods for Computer VisionDaniel Cremers
Middlebury Benchmark
8Global Optimization Methods for Computer VisionDaniel Cremers
Multiple View Reconstruction
Kolev, Cremers, ECCV ’08
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Multiple View Reconstruction
10Global Optimization Methods for Computer VisionDaniel Cremers
Super-Resolution Texture Map
Goldlücke, Cremers, ICCV ’09, DAGM ’09* * Best Paper
Award
11Global Optimization Methods for Computer VisionDaniel Cremers
Super-Resolution Texture Map
Goldlücke, Cremers, ICCV ’09, DAGM ‘09
Weighted average Super-resolution texture
12Global Optimization Methods for Computer VisionDaniel Cremers
One of two images Depth reconstruction
Convex Multilabel Optimization
Pock, Schoenemann, Graber, Bischof, Cremers, ECCV ’08
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Convex Multilabel Optimization
Pock, Chambolle, Bischof, Cremers, CVPR ’09
Input color image 10 label segmentation
14Global Optimization Methods for Computer VisionDaniel Cremers
Motion analysis3D reconstruction
Shape analysis Segmentation & tracking
Overview
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Input video
High Accuracy Motion Estimation
Realtime optical flow
16Global Optimization Methods for Computer VisionDaniel Cremers
Input video
Realtime optical flow *
* 30 fps at 640 x 480 resolution
High Accuracy Motion Estimation
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Quantitative Performance
Wedel et al. ICCV ’09
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Wedel et al., ECCV ’08
Scene Flow: Motion & 3D StructureIn collaboration with Daimler Research
19Global Optimization Methods for Computer VisionDaniel Cremers
Motion analysis3D reconstruction
Shape analysis Segmentation & tracking
Overview
20Global Optimization Methods for Computer VisionDaniel Cremers
Klassen et al., PAMI ’03:Shape similarity as length of the shortest path (geodesic)
Shape Similarity via Geodesics
21Global Optimization Methods for Computer VisionDaniel Cremers
Schmidt, Clausen, Cremers DAGM ’06
Variational length minimizationShooting Methode
symmetric, stable, 1000 x faster
Shape Similarity via Geodesics
22Global Optimization Methods for Computer VisionDaniel Cremers
Schmidt, Farin, Cremers, ICCV ’07
Optimal correspondence betweenPoints of similar curvature
Optimal Matching & Correspondence
Matching as shortest cylic path
23Global Optimization Methods for Computer VisionDaniel Cremers
Fast Matching in Subcubic Runtime
24Global Optimization Methods for Computer VisionDaniel Cremers
Motion analysis3D reconstruction
Shape analysis Segmentation & tracking
Overview
25Global Optimization Methods for Computer VisionDaniel Cremers
Schoenemann, Cremers ICCV 2007, PAMI 2009 Theorem: Any matching of the template to the image corresponds to
a cyclic path in the product space of template and image.
template
?
Globally Optimal Tracking
26Global Optimization Methods for Computer VisionDaniel Cremers
25 fps3 fps
Schoenemann & Cremers CVPR ’08:Real-time by parallel implementation of polynomial algorithm.
Globally Optimal Tracking
27Global Optimization Methods for Computer VisionDaniel Cremers
Globally Optimal Tracking
28Global Optimization Methods for Computer VisionDaniel Cremers
Tracking deformable shapes
Multiple view reconstruction
Scene flow estimation
Summary
Super-resolution texturing