andrew nealen and marc alexa, discrete geometric modeling group, tu darmstadt, 2004 fast and high...
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Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast and High Quality Overlap Fast and High Quality Overlap Repair for Patch-Based Texture Repair for Patch-Based Texture
SynthesisSynthesis
Andrew NealenAndrew NealenMarc AlexaMarc Alexa
Discrete Geometric Modeling Group (DGM)Discrete Geometric Modeling Group (DGM)Technische Universität DarmstadtTechnische Universität Darmstadt
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Our Setting: 2D Texture Synthesis
nxm Input Texture
NxM Output Texture
► The goal: Synthesize an output texture which is perceptually similar to the input texture. Also ensure that the result contains sufficient variation.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Patch-Based Texture Synthesis
Some Existing Methods
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
__22
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
__ ==22
overlap error
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
__ ==22
overlap error
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
__ ==22
overlap error
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
__ ==22
overlap error
A B
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► A Very Popular 2D Texture Synthesis Method• Image Quilting [Efros and Freeman 2001]• Graphcut Texures [Kwatra et. al 2003]• Wang Tiles [Cohen et. al 2003]
Patch-Based Texture Synthesis
Some Existing Methods
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► Introduced at EGSR 2003 [Nealen and Alexa]
• Adaptive Patch Sampling, like Hierarchical Pattern Mapping [Soler et. al 2002]
• Per-Pixel Overlap Re-synthesis
Patch-Based Texture Synthesis
Hybrid Texture Synthesis (HTS)
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Hybrid Texture SynthesisMethod
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Goal:From nxm, synthesize
NxM
similar, but not identical
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Result (N x M)Result (N x M)
Goal:From nxm, synthesize
NxM
similar, but not identical
Input (n x m)
Intermediate Result
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Goal:From nxm, synthesize
NxM
similar, but not identical
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Patch-Search in the Input + Copy to Result + Mark Invalid Pixels
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Goal:From nxm, synthesize
NxM
similar, but not identical
Patch-Search in the Input + Copy to Result + Mark Invalid Pixels
Per-Pixel Re-synthesis Steps (for each Patch)
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Goal:From nxm, synthesize
NxM
similar, but not identical
Patch-Search in the Input + Copy to Result + Mark Invalid Pixels
Per-Pixel Re-synthesis Steps (for each Patch)
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Goal:From nxm, synthesize
NxM
similar, but not identical
Patch-Search in the Input + Copy to Result + Mark Invalid Pixels
Per-Pixel Re-synthesis Steps (for each Patch)
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Goal:From nxm, synthesize
NxM
similar, but not identical
Patch-Search in the Input + Copy to Result + Mark Invalid Pixels
Per-Pixel Re-synthesis Steps (for each Patch)
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Goal:From nxm, synthesize
NxM
similar, but not identical
Result (N x M)
Input (n x m)
Intermediate Result
Result (N x M)
Patch-Search in the Input + Copy to Result + Mark Invalid Pixels
Per-Pixel Re-synthesis Steps (for each Patch)
Hybrid Texture SynthesisMethod
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Hybrid Texture SynthesisGeneralization: Pro and Con
► Pro: General Method for Overlap Repair• Complementary to other Methods, such as
Minimum-Error-Boundary-Cut (MEBC) or Feathering, yet oftentimes produces better results
• Generalizes to arbitrary patch shapes, i.e. is applicable to Graphcut Textures, Wang Tiles, etc.
► Con: Computationally Expensive• Exhaustive search for each invalid pixel in the
overlap, based on mostly irregular valid neighborhood
• Has O(rN log N) complexity -> Doesn‘t scale well.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Hybrid Texture SynthesisGeneralization: Pro and Con
► Pro: General Method for Overlap Repair• Complementary to other Methods, such as
Minimum-Error-Boundary-Cut (MEBC) or Feathering, yet oftentimes produces better results
• Generalizes to arbitrary patch shapes, i.e. is applicable to Graphcut Textures, Wang Tiles, etc.
► Con: Computationally Expensive• Exhaustive search for each invalid pixel in the
overlap, based on mostly irregular valid neighborhood
• Has O(rN log N) complexity -> Doesn‘t scale well.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Hybrid Texture SynthesisGeneralization: Pro and Con
► Pro: General Method for Overlap Repair• Complementary to other Methods, such as
Minimum-Error-Boundary-Cut (MEBC) or Feathering, yet oftentimes produces better results
• Generalizes to arbitrary patch shapes, i.e. is applicable to Graphcut Textures, Wang Tiles, etc.
► Con: Computationally Expensive• Exhaustive search for each invalid pixel in the
overlap, based on mostly irregular valid neighborhood
• Has O(rN log N) complexity -> Doesn‘t scale well.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap RepairBasic Idea
► Inspiration• Ashikhmin: Synthesizing Natural Textures
[2001] termed Coherence Search• Tong et. al‘s extension: k-Coherence Search
[2002]
► Basic Idea: Intelligently Reduce Search Space• Only search within a set of coherent pixels• Introduce Trade-off between quality and speed
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
► Applying Coherence Search• For each pixel in the output, store its location in
the input in a source map (same size as the output texture)
Input Texture
Intermediate Result + Source Map
Fast Overlap Repair Coherence Search
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair Coherence Search
► Applying Coherence Search• When searching for a new pixel, only consider
input pixels which are coherent with neighboring output pixels
Input Texture
Source Map Lookup
Intermediate Result + Source Map
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair Coherence Search
► Applying Coherence Search• When searching for a new pixel, only consider
input pixels which are coherent with neighboring output pixels
Input Texture
Intermediate Result + Source Map
Source Map Lookup
Consequence in this example: Only two possible candidates
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair Coherence Search
► Applying Coherence Search• Simply comparing to the coherent pixels results in
seams similar to Image Quilting (MEBC)
Example:
64x64 Texture Synthesized from four 32x32 Patches Coherence Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair Coherence Search
► Applying Coherence Search• Simply comparing to the coherent pixels results in
seams similar to Image Quilting (MEBC)
Example:
64x64 Texture Synthesized from four 32x32 Patches Coherence Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair Coherence Search
► Applying Coherence Search• Simply comparing to the coherent pixels results in
seams similar to Image Quilting (MEBC)
Example:
64x64 Texture Synthesized from four 32x32 Patches Coherence Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair k-Coherence Search
Input Texture
Intermediate Result + Source Map
Source Map Lookup
► Better: Applying k-Coherence Search
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair k-Coherence Search
► Better: Applying k-Coherence Search• Extend the set by the k-nearest neighbors (knn) of
each coherent pixel (in feature space) and remove duplicates
Intermediate Result + Source Map
Source Map Lookup
Input Texture
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair k-Coherence Search
► Precomputation of knn Data Structure• Performed once for each nxm input texture and
stored for repeated use
• User defines size of box-shaped neighborhood np
• For each of the nxm input pixels─ Construct feature vector by ordered concatenation of the npx np
RGB-triples in the box-shaped neighborhood
• Dimension reduction (75-90%) by applying PCA• Compute indices of k-nearest neighbors to each
pixel
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Fast Overlap Repair k-Coherence Search
► Source Map Maintenance• Each valid pixel in the overlap region is a linear
blend (feathering) of at least two original pixel values, i.e. from at least two different sources
• To avoid the maintenance of multiple source maps, simply store the source of the pixel with greatest contribution in a single source map
Blue: invalid overlap pixels
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results varying k
k = 1
k = 11
k = 4
Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results varying k
k = 1
k = 11
k = 4
Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results varying k
k = 1
k = 11
k = 4
Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results varying k
k = 1
k = 11
k = 4
Exhaustive
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results timings
InputExhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
scales 64×64 δmax = 0.02 Δmax = 0.05
rock 128×128 δmax = 0.02 Δmax = 0.05
stonewall 200×200
δmax = 0.02 Δmax = 0.03
Pre: 0 sec.
Synth: 283 sec.
Pre: 0 sec.
Synth: 533 sec.
Pre: 0 sec.
Synth: 985 sec.
Pre: 6+3 sec.
Synth: 226 sec.
Pre: 45+62 sec.
Synth: 226 sec.
Pre: 247+28 s
Synth: 178 sec.
Pre: 6+4 sec.
Synth: 427 sec.
Pre: 45+74 sec.
Synth: 415 sec.
Pre: 247+37 s
Synth: 350 sec.
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results
Exhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Results
Exhaustive
n = 7x7
k-Coherence
n = 3x3 | k = 5
k-Coherence
n = 5x5 | k = 11
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
ResultsSynthesis Comparisons
Input
Efros/Leung Wei/Levoy
IQ PBS HTS
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Conclusions and Future Work
► Improve Error Metric• Still using the L2 norm due to its simplicity
• Develop a metric which takes feature mismatch into account
• Texton map approach [Zhang et al. 2003]• Feature Map [Wu and Yu 2004] performs even
better, and for near-regular textures, see [Liu et. al 2004] (both to appear at SIGGRAPH 2004)
Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004
Questions ?
► Contact Information
Andrew [email protected]
Marc [email protected]
http://www.dgm.informatik.tu-darmstadt.de
Matlab code:http://www.dgm.informatik.tu-darmstadt.de/research/texsynth.html