stereo matching using dynamic programming

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Stereo Matching Using Stereo Matching Using Dynamic Programming Dynamic Programming Jim Rehg Jim Rehg CS 4495/7495 Computer Vision CS 4495/7495 Computer Vision Lecture 4 Lecture 4 Mon Sept 2, 2002 Mon Sept 2, 2002

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Stereo Matching Using Dynamic Programming. Jim Rehg CS 4495/7495 Computer Vision Lecture 4 Mon Sept 2, 2002. Correspondence. It is fundamentally ambiguous, even with stereo constraints. Ordering constraint…. …and its failure. Occluded Pixels. Dis-occluded Pixels. - PowerPoint PPT Presentation

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Stereo Matching Using Dynamic Stereo Matching Using Dynamic ProgrammingProgramming

Jim RehgJim Rehg

CS 4495/7495 Computer VisionCS 4495/7495 Computer Vision

Lecture 4Lecture 4

Mon Sept 2, 2002Mon Sept 2, 2002

2 J. M. Rehg © 2002

CorrespondenceCorrespondence

It is fundamentally ambiguous, even with stereo It is fundamentally ambiguous, even with stereo constraintsconstraints

Ordering constraint… …and its failure

3 J. M. Rehg © 2002

Search Over CorrespondencesSearch Over Correspondences

Three cases:Three cases: Sequential – cost of matchSequential – cost of match Occluded – cost of no matchOccluded – cost of no match Disoccluded – cost of no matchDisoccluded – cost of no match

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4 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Dynamic programming Dynamic programming yields the optimal path yields the optimal path through grid. This is through grid. This is the best set of the best set of matches that satisfy matches that satisfy the ordering constraintthe ordering constraint

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Dynamic ProgrammingDynamic Programming

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1tC tC 1tC

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Principle of Optimality for an n-stage assignment problem:

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Dynamic ProgrammingDynamic Programming

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1tC tC 1tC

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Principle of Optimality for an n-stage assignment problem:

)(maxarg)( 1 iCjC tijit

2j

1i

2i

3i

7 J. M. Rehg © 2002

Dynamic ProgrammingDynamic Programming

1

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1tC tC 1tC

Principle of Optimality for an n-stage assignment problem:

)(maxarg)( 1 iCjC tijit

3j

1i

2i

3i

13

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8 J. M. Rehg © 2002

Dynamic ProgrammingDynamic Programming

1

2

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1

2

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1

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1tC tC 1tC

Principle of Optimality for an n-stage assignment problem:

)(maxarg)( 1 iCjC tijit

3j

1i

2i

3i1)3( tb

9 J. M. Rehg © 2002

Dynamic ProgrammingDynamic Programming

1

2

3

1

2

3

1

2

3

1tC tC 1tC

Principle of Optimality for an n-stage assignment problem:

)(maxarg)( 1 iCjC tijit

1i

2i

3i1)3( tb

2)2( tb

1j1)1( tb

10 J. M. Rehg © 2002

Dynamic ProgrammingDynamic Programming

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2TC 1TC TC

Back-chaining recovers the optimal path and its cost:

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11 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

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Terminal

12 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

Occluded Pixels

Left scanline

Dis-occluded Pixels

Right scanline

Terminal

13 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

Occluded Pixels

Left scanline

Dis-occluded Pixels

Right scanline

Terminal

14 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

Occluded Pixels

Left scanline

Dis-occluded Pixels

Right scanline

Terminal

15 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

Occluded Pixels

Left scanline

Dis-occluded Pixels

Right scanline

Terminal

16 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

Occluded Pixels

Left scanline

Dis-occluded Pixels

Right scanline

Terminal

17 J. M. Rehg © 2002

Stereo Matching with Dynamic Stereo Matching with Dynamic ProgrammingProgramming

Scan across grid Scan across grid computing optimal cost computing optimal cost for each node given its for each node given its upper-left neighbors.upper-left neighbors.Backtrack from the Backtrack from the terminal to get the terminal to get the optimal path.optimal path.

Occluded Pixels

Left scanline

Dis-occluded Pixels

Right scanline

Terminal

18 J. M. Rehg © 2002

Computing CorrespondenceComputing Correspondence

Another approach is to match Another approach is to match edgesedges rather than rather than windows of pixels:windows of pixels:

Which method is better?Which method is better? Edges tend to fail in dense texture (outdoors)Edges tend to fail in dense texture (outdoors) Correlation tends to fail in smooth featureless areasCorrelation tends to fail in smooth featureless areas

19 J. M. Rehg © 2002

Computing CorrespondencesComputing Correspondences

Both methods fail for smooth surfacesBoth methods fail for smooth surfaces

There is currently no good solution to the There is currently no good solution to the correspondence problemcorrespondence problem