Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Hybrid Texture Synthesis Andrew Nealen Marc Alexa Discrete Geometric.

Slides:

Advertisements

Similar presentations

Filling Algorithms Pixelwise MRFsChaos Mosaics Patch segments are pasted, overlapping, across the image. Then either: Ambiguities are removed by smoothing.

Advertisements

Image Quilting and Apples

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2004 Fast and High Quality Overlap Repair for Patch-Based Texture Synthesis.

QR Code Recognition Based On Image Processing

Texture. Limitation of pixel based processing Edge detection with different threshold.

Data-driven methods: Texture (Sz 10.5) Cs129 Computational Photography James Hays, Brown, Spring 2011 Many slides from Alexei Efros.

November 12, 2013Computer Vision Lecture 12: Texture 1Signature Another popular method of representing shape is called the signature. In order to compute.

Texture Synthesis on [Arbitrary Manifold] Surfaces Presented by: Sam Z. Glassenberg* * Several slides borrowed from Wei/Levoy presentation.

Image Processing A brief introduction (by Edgar Alejandro Guerrero Arroyo)

Document Image Processing

Texture. Edge detectors find differences in overall intensity. Average intensity is only simplest difference. many slides from David Jacobs.

1.  Texturing is a core process for modeling surface details in computer graphics applications › Texture mapping › Surface texture synthesis › Procedural.

More details on presentations Aim to speak for ~50 min (after 15 min review, leaving 10 min for discussions) Try to plan discussion topics It’s fine to.

Texture Synthesis Tiantian Liu. Definition Texture – Texture refers to the properties held and sensations caused by the external surface of objects received.

Improved Image Quilting Jeremy Long David Mould. Introduction   Goal: improve “ minimum error boundary cut ”

Example Based 3D Shape Completion Mark Pauly 1,2, Niloy J. Mitra 1, Joachim Giesen 2, Markus Gross 2, Leonidas J. Guibas 1 1 Stanford University 2 ETH,

Maryia Kazakevich “Texture Synthesis by Patch-Based Sampling” Texture Synthesis by Patch-Based Sampling Real-Time Texture Synthesis By Patch-Based Sampling,

Image Quilting for Texture Synthesis & Transfer Alexei Efros (UC Berkeley) Bill Freeman (MERL) +=

Overview of Texture Synthesis Ganesh Ramanarayanan Cornell Graphics Seminar.

Order-Independent Texture Synthesis Li-Yi Wei Marc Levoy Gcafe 1/30/2003.

Lapped Textures SIGGRAPH 2000 Emil Praun Adam Finkelstein Hugues Hoppe.

Image Quilting for Texture Synthesis and Transfer Alexei A. Efros1,2 William T. Freeman2.

Quadtrees, Octrees and their Applications in Digital Image Processing

Lecture 1: Images and image filtering

Chamfer Matching & Hausdorff Distance Presented by Ankur Datta Slides Courtesy Mark Bouts Arasanathan Thayananthan.

Announcements For future problems sets: matlab code by 11am, due date (same as deadline to hand in hardcopy). Today’s reading: Chapter 9, except.

Texture Reading: Chapter 9 (skip 9.4) Key issue: How do we represent texture? Topics: –Texture segmentation –Texture-based matching –Texture synthesis.

Texture Synthesis from Multiple Sources Li-Yi Wei Stanford University (was) NVIDIA Corporation (now)

Near-Regular Texture Analysis and Manipulation Written by: Yanxi Liu Yanxi Liu Wen-Chieh Lin Wen-Chieh Lin James Hays James Hays Presented by: Alex Hadas.

Image Pyramids and Blending

Texture Optimization for Example-based Synthesis

Image Analogies Aaron Hertzmann (1,2) Charles E. Jacobs (2) Nuria Oliver (2) Brian Curless (3) David H. Salesin (2,3) 1 New York University 1 New York.

Multiclass object recognition

SIGGRAPH 2003 Jingdan Zhang, Kun Zhou, Luiz Velho, Baining Guo, Heung-Yeung Shum.

Projective Texture Atlas for 3D Photography Jonas Sossai Júnior Luiz Velho IMPA.

Context-based Surface Completion Andrei Sharf, Marc Alexa, Daniel Cohen-Or.

Efficient Editing of Aged Object Textures By: Olivier Clément Jocelyn Benoit Eric Paquette Multimedia Lab.

Light Using Texture Synthesis for Non-Photorealistic Shading from Paint Samples. Christopher D. Kulla, James D. Tucek, Reynold J. Bailey, Cindy M. Grimm.

Continuous Model Synthesis Paul Merrell and Dinesh Manocha In SIGGRAPH Asia 2008 발표 : 이성호.

Texture Optimization for Example-based Synthesis Vivek Kwatra Irfan Essa Aaron Bobick Nipun Kwatra.

CS 4487/6587 Algorithms for Image Analysis

Image Quilting for Texture Synthesis and Transfer Alexei A. Efros (UC Berkeley) William T. Freeman (MERL) Siggraph01 ’

Synthesis of Compact Textures for real-time Terrain Rendering Nader Salman 22 juin 2007 Encadrant : Sylvain Lefebvre.

TextureAmendment Reducing Texture Distortion in Constrained Parameterizations Yu-Wing TaiNational University of Singapore Michael S. BrownNational University.

Course 2 Image Filtering. Image filtering is often required prior any other vision processes to remove image noise, overcome image corruption and change.

Synthesizing Natural Textures Michael Ashikhmin University of Utah.

Duy & Piotr. How to reconstruct a high quality image with the least amount of samples per pixel the least amount of resources And preserving the image.

Towards Real-Time Texture Synthesis With the Jump Map Steve Zelinka Michael Garland University of Illinois at Urbana-Champaign Thirteenth Eurographics.

Mesh Quilting For Geometric Texture Synthesis Kun Zhou et al. In SIGGRAPH 2006 발표 이성호 2009 년 4 월 15 일.

2D Texture Synthesis Instructor: Yizhou Yu. Texture synthesis Goal: increase texture resolution yet keep local texture variation.

Non-Ideal Iris Segmentation Using Graph Cuts

Two Patch-based Algorithms for By-example Texture Synthesis Bruno Galerne MAP5, Université Paris Descartes 1 Master 2 Traitement.

Geometry Synthesis Ares Lagae Olivier Dumont Philip Dutré Department of Computer Science Katholieke Universiteit Leuven 10 August, 2004.

Texture Synthesis by Image Quilting CS766 Class Project Fall 2004 Eric Robinson.

Speaker Min-Koo Kang March 26, 2013 Depth Enhancement Technique by Sensor Fusion: MRF-based approach.

Image features and properties. Image content representation The simplest representation of an image pattern is to list image pixels, one after the other.

SIGGRAPH 2007 Hui Fang and John C. Hart.  We propose an image editing system ◦ Preserve its detail and orientation by resynthesizing texture from the.

Texture Analysis and Synthesis. Texture Texture: pattern that “looks the same” at all locationsTexture: pattern that “looks the same” at all locations.

Two Patch-based Algorithms for By-example Texture Synthesis

A Plane-Based Approach to Mondrian Stereo Matching

- photometric aspects of image formation gray level images

Detail Preserving Shape Deformation in Image Editing

Image gradients and edges

Texture Quality Extensions to Image Quilting

CSE 455 – Guest Lectures 3 lectures Contact Interest points 1

Image Quilting for Texture Synthesis & Transfer

A Computational Darkroom for BW Photography

Lecture 1: Images and image filtering

Photometric Processing

Presentation transcript:

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Hybrid Texture Synthesis Andrew Nealen Marc Alexa Discrete Geometric Modeling Group (DGM) Technische Universität Darmstadt

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, D Texture Synthesis n x m Input Texture N x M 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, 2003 Our Inspiration ► Existing (and Impressive) Technology Pixel-Based: ─Non-parametric Sampling [Efros and Leung 1999] ─Tree-structured Vector Quantization [Wei and Levoy 2000] ─Synthesizing Natural Textures [Ashikhmin 2001] ─Image Analogies [Hertzmann et al. 2001] Patch-Based: ─Patch-Based Sampling [Guo et al. 2001] ─Image Quilting [Efros and Freeman 2001] ─Hierarchical Pattern Mapping [Soler et al. 2002]

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Our Inspiration ► Possible Drawbacks ? Loss of global structure Loss of scale Boundary mismatch Blurring

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Our Inspiration ► Possible Drawbacks ? Loss of global structure Loss of scale Boundary mismatch Blurring Wei/Levoy Algorithm: Can occur when using structured Textures with rich histograms. This is due to the L 2 norm.

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Our Inspiration ► Possible Drawbacks ? Loss of global structure Loss of scale Boundary mismatch Blurring Patch-Based Sampling Algorithm: Can occur when texture features are not well-aligned across patch boundaries

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Our Inspiration ► Possible Drawbacks ? Loss of global structure Loss of scale Boundary mismatch Blurring ► Is it possible to reduce all deficiencies for an even larger class of textures ?

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput Step n in Algorithm

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput Build L-shaped Neighborhood

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput Find Matching Neighborhood(s) in Input

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput Select Best Neighborhood from all Candidates

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput Copy Selected Pixel to Output

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput Repeat for all Pixels in this Row...

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis InputOutput...and all other Output Rows

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Pixel-Based Texture Synthesis ► Main difference between Efros/Leung (EL), Wei/Levoy (WL) and Ashikhmin (AS): Per- Pixel Search Strategy. ► Ashikhmin’s insight: “WL tends to produce textures with smoothed out edges in some cases.” ► Ashikhmin’s solution: consider only coherent pixels in the search, which encourages region growing. ► Problem: inconsistencies can still occur for highly-structured textures.

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Step n in Algorithm

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Patch to be Synthesized in this Step

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Build Neighborhood (Image Mask)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Find Matching Neighborhood(s) in Input

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Select Best Neighborhood from all Candidates

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Copy Selected Patch to Output

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Apply some Overlap Repair Strategy...

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput... And we are done with this patch

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis InputOutput Repeat for all Patches in Output

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis Generalization of Pixel-Based Synthesis ► Main (visual) difference between Image Quilting (IQ) and Patch-based Sampling (PBS): Overlap Repair Strategy. ► IQ performs a minimum-error-boundary- cut (MEBC) through the error surface (least cost path; Dijkstra). ► PBS feathers the overlap region (alpha- blending). ► Pro: patch-based algorithms are good at maintaining global structure. ► Problems: boundary mismatch (MEBC, top) and blurring (PBS, bottom).

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis Generalization of Pixel-Based Synthesis ► Main (visual) difference between Image Quilting (IQ) and Patch-based Sampling (PBS): Overlap Repair Strategy. ► IQ performs a minimum-error-boundary- cut (MEBC) through the error surface (least cost path; Dijkstra). ► PBS feathers the overlap region (alpha- blending). ► Pro: patch-based algorithms are good at maintaining global structure. ► Problems: boundary mismatch (MEBC, top) and blurring (PBS, bottom).

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Patch-Based Texture Synthesis Generalization of Pixel-Based Synthesis ► Main (visual) difference between Image Quilting (IQ) and Patch-based Sampling (PBS): Overlap Repair Strategy. ► IQ performs a minimum-error-boundary- cut (MEBC) through the error surface (least cost path; Dijkstra). ► PBS feathers the overlap region (alpha- blending). ► Pro: patch-based algorithms are good at maintaining global structure. ► Problems: boundary mismatch (MEBC, top) and blurring (PBS, bottom).

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Hybrid Texture Synthesis Idea ► Combine best aspects from other approaches while avoiding (or improving over) known pitfalls: Patch-based algorithms ─ Are good at preserving global structure ─ Can introduce artifacts along patch boundaries Pixel-based algorithms ─ Preserve local coherence (MRF model: local and stationary) ─ Possibly fail to preserve global structure ─ This is especially problematic for textures with rich histograms and many high frequency features, due to the smoothing nature of the distance metric (L 2 norm)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Hybrid Texture Synthesis (HTS) Algorithm ► Hybrid, two-fold approach to texture synthesis Adaptive patch sampling [Soler et al. 2002] ─ Start with a uniform quadrilateral grid of patches (the output) ─ Recursively split a patch if the best fit taken from the input texture is not good enough (a user-defined tradeoff  max ) Overlap re-synthesis (novel overlap repair strategy) ─ Mark invalid pixels in the overlap region ─ Validity map is defined by the error surface E(x)=(x src – x dst ) 2 and a user-defined tolerance parameter  max ─ Define an ordering for the invalid pixels and re-synthesize them using a per-pixel strategy

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Adaptive Patch Sampling Principle ► Essentially: Hierarchical Pattern Mapping applied to the plane [Soler et al. 2002] ► Analogy: Assembling a quadtree puzzle  p >  max  Split Patch Synthesize White Patch  p <  max

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Adaptive Patch Sampling Trade-off ► Trade-off between preserving global structure and avoiding detail artifacts  max = 1.0  max = 0.01  max = 0.04

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Adaptive Patch Sampling Trade-off ► Trade-off between preserving global structure and avoiding detail artifacts  max = 1.0  max = 0.01  max = 0.04

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Adaptive Patch Sampling Trade-off ► Trade-off between preserving global structure and avoiding detail artifacts  max = 1.0  max = 0.01  max = 0.04

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Adaptive Patch Sampling Trade-off ► Trade-off between preserving global structure and avoiding detail artifacts  max = 1.0  max = 0.01  max = 0.04

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Synthesizing a single patch Synthesize Black Patch i

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Extracting the image mask Synthesize Black Patch i ► Grow patch by overlap (toroidally)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Extracting the image mask Image Mask (I i )Synthesize Black Patch iBinary Support (J i ) ► Extract image mask (I i ) and binary support (J i ), and circularly shift to upper left corner

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Computing the error image Image Mask (I i )Input Texture (T)Binary Support (J i ) ► Compute error E i (x 0 ) between I i and T for each circular shift x 0 =(  x,  y) of the input texture T

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Computing the error image Error Image (E i )Input Texture (T)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Selecting a Patch Error Image (E)Input Texture (T)Selected Patch (P)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 In Search of Good Patches Selecting a Patch Error Image (E)Input Texture (T)Selected Patch (P) ► Note: error image can be computed efficiently in the Fourier domain. Complexity: O(n log n) per patch.

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Overlap error and pixel invalidation Patch and Image MaskSelected Patch (P)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Overlap error and pixel invalidation Image Mask (I)Selected Patch (P)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Overlap error and pixel invalidation Image Mask (I)Selected Patch (P) Error Surface (S)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Overlap error and pixel invalidation Patch and Image MaskP with Invalid Pixels (blue) Error Surface (S) ► Trade-off: user-defined  max is the pixel error tolerance. Setting to 1 results in pure feathering.

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Compositing Intermediate ResultPatch with Invalid Pixels P OVER R i-1

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Compositing Composited ResultPatch with Invalid Pixels ► Simple scanline re-synthesis: possibly insufficient causal neighborhood within valid pixel region ► Solution: alternative ordering (Pixel Traversal Map)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Pixel Traversal Map (Ordering) Composited ResultPatch with Invalid Pixels Pixel Traversal Map (M) ► Pixel Traversal Map: repeated morphological dilation of the binary support for valid pixels with a euclidian disk of radius 1 (city-block distance transform).

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Pixel Traversal Map (Ordering) Composited ResultPatch with Invalid Pixels Pixel Traversal Map (M) ► Pixel Traversal Map: step 1

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Pixel Traversal Map (Ordering) Composited ResultPatch with Invalid Pixels Pixel Traversal Map (M) ► Pixel Traversal Map: step 2...

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Pixel Traversal Map (Ordering) Composited ResultPatch with Invalid Pixels Pixel Traversal Map (M) ► Pixel Traversal Map:... step n

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Pixel Traversal Map (Ordering) Composited ResultPatch with Invalid Pixels Pixel Traversal Map (M) ► Art Restorer Analogy: stepwise restoration of the hole from the boundary of the existing image.

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Pixel Traversal Map (M) ► Synthesize red pixel from valid (or already re- synthesized) pixels Intermediate Result

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Image Mask (I j ) Binary Support (J j ) ► Extract image mask (I j ) and binary support (J j ) for best- pixel search in the input texture. Complexity: O(n log n) per pixel. Intermediate Result

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Intermediate ResultPixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Intermediate Result Shifted Pixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Intermediate Result with Traversal Map Pixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Overlap Re-synthesis Step 1 Pixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Overlap Re-synthesis Step 2 Pixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Overlap Re-synthesis Step 3 Pixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Overlap Re-synthesis Per-Pixel Re-synthesis Overlap Re-synthesis Result Pixel Traversal Map (M)

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Overlap Repair Comparisons No RepairHTSPBSIQ

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input Efros/LeungWei/Levoy IQPBSHTS

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input PBSHTS PBSHTS Input

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input PBSHTS PBSHTS Input

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results Synthesis Comparisons Input PBSHTS PBSHTS Input

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Results

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Conclusions and Future Work ► Improve Computational Complexity Pixel neighborhoods of patch and pixel stage are not known a priori, so precomputation is not straightforward We can solve this in the pixel stage by employing k- coherence search [Tong et al. 2002] [Ashikhmin 2001] ► Improve Error Metric Still using the L 2 norm due to its simplicity Develop a metric which takes feature mismatch into account Texton approach looks promising [Zhang et al. 2003]

Andrew Nealen and Marc Alexa, Discrete Geometric Modeling Group, TU Darmstadt, 2003 Questions ? ► Contact Information Andrew Nealen Marc Alexa Matlab code and extended Thesis: