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Material Appearance Modeling : A Data-Coherent Approach

By: Dong, Yue.
Contributor(s): Lin, Stephen | Guo, Baining.
Material type: TextTextSeries: eBooks on Demand.Publisher: Dordrecht : Springer, 2013Description: 1 online resource (182 p.).ISBN: 9783642357770.Subject(s): Computer graphics | Computer vision | Image processing -- Digital techniquesGenre/Form: Electronic books.Additional physical formats: Print version:: Material Appearance Modeling : A Data-Coherent ApproachDDC classification: 006.6 Online resources: Click here to view this ebook.
Contents:
Material Appearance Modeling: A Data-Coherent Approach; Preface; Contents; Chapter 1: Introduction; 1.1 Background; 1.1.1 Fundamentals of Light Interaction with Materials; 1.1.2 Taxonomy of Light Scattering Functions; 1.1.3 Modeling and Rendering Pipeline of Material Appearance; 1.2 Data Coherence for Appearance Modeling; 1.2.1 Data Coherence; 1.2.2 Coherence-Based Reconstruction; 1.2.3 A General Framework for Coherence-Based Appearance Modeling; 1.3 Overview; 1.3.1 Acquisition and Modeling of Opaque Surfaces; 1.3.2 Modeling and Rendering of Subsurface Light Transport
1.3.3 Material FabricationReferences; Part I: Acquisition and Modeling of Opaque Surfaces; Chapter 2: Surface Reflectance Overview; 2.1 Surface Reflectance Acquisition; 2.1.1 Direct Measurement; 2.1.2 Angular Coherence; 2.1.3 Spatial Coherence; 2.2 Interactive Modeling and Editing; References; Chapter 3: Efficient SVBRDF Acquisition with Manifold Bootstrapping; 3.1 Related Work; 3.2 SVBRDF Manifold Bootstrapping; 3.2.1 Representative and Key Measurement; Representative Measurement; Key Measurement; 3.2.2 Manifold Bootstrapping Overview; Local BRDF Reconstruction
Representative Projection and Bootstrapping3.2.3 Manifold Bootstrapping Details; Estimating Local BRDF Dimensionality; Uniform Measurement Scaling; Neighborhood Selection; Local Linear Combination; 3.2.4 Synthetic Enlargement for Representatives; 3.2.5 Key Measurement Validation; 3.3 SVBRDF Data Acquisition; 3.3.1 Acquiring Representatives: BRDF Samples; Device Setup; Calibration; Capturing; Reconstruction; 3.3.2 Acquiring Keys: Reflectance Maps; Key Lighting Dimensionality; Key Lighting Size; 3.4 Experimental Results; 3.4.1 Method Validation; Test on Fully-Sampled SVBRDF
Comparison with Microfacet SynthesisEffect of Neighborhood Size; 3.4.2 SVBRDF Capture Results; 3.5 Conclusion; References; Chapter 4: Interactive SVBRDF Modeling from a Single Image; 4.1 Related Work; 4.2 System Overview; 4.3 User-Assisted Shading Separation; 4.3.1 Separation as Optimization; 4.3.2 Interactive Refinement; User Strokes; Result Refinement; 4.3.3 Discussion; 4.4 Two-Scale Normal Reconstruction; Discussion; 4.5 User-Assisted Specular Assignment; Material Classification; Specular Coefficient Assignment; 4.6 Experimental Results; Performance; User Input
Comparison with Standard ToolsetsResults; Limitations; 4.7 Conclusion; References; Part II: Modeling and Rendering of Subsurface Light Transport; Chapter 5: Overview of Subsurface Light Transport; 5.1 Computing Subsurface Light Transport; 5.2 Capturing Subsurface Scattering Effects; References; Chapter 6: Modeling Subsurface Light Transport with the Kernel Nyström Method; 6.1 Related Work; 6.2 The Kernel Nyström Method; 6.2.1 Kernel Extension; 6.2.2 Estimating the Light Transport Kernel f; 6.3 Adaptive Light Transport Measurement; Device Setup and Calibration; Column Sampling
6.4 Results and Discussions
Summary: A principal aim of computer graphics is to generate images that look as real as photographs. Realistic computer graphics imagery has however proven to be quite challenging to produce, since the appearance of materials arises from complicated physical processes that are difficult to analytically model and simulate, and image-based modeling of real material samples is often impractical due to the high-dimensional space of appearance data that needs to be acquired.This book presents a general framework based on the inherent coherency in the appearance data of materials to make image-based appeara
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Material Appearance Modeling: A Data-Coherent Approach; Preface; Contents; Chapter 1: Introduction; 1.1 Background; 1.1.1 Fundamentals of Light Interaction with Materials; 1.1.2 Taxonomy of Light Scattering Functions; 1.1.3 Modeling and Rendering Pipeline of Material Appearance; 1.2 Data Coherence for Appearance Modeling; 1.2.1 Data Coherence; 1.2.2 Coherence-Based Reconstruction; 1.2.3 A General Framework for Coherence-Based Appearance Modeling; 1.3 Overview; 1.3.1 Acquisition and Modeling of Opaque Surfaces; 1.3.2 Modeling and Rendering of Subsurface Light Transport

1.3.3 Material FabricationReferences; Part I: Acquisition and Modeling of Opaque Surfaces; Chapter 2: Surface Reflectance Overview; 2.1 Surface Reflectance Acquisition; 2.1.1 Direct Measurement; 2.1.2 Angular Coherence; 2.1.3 Spatial Coherence; 2.2 Interactive Modeling and Editing; References; Chapter 3: Efficient SVBRDF Acquisition with Manifold Bootstrapping; 3.1 Related Work; 3.2 SVBRDF Manifold Bootstrapping; 3.2.1 Representative and Key Measurement; Representative Measurement; Key Measurement; 3.2.2 Manifold Bootstrapping Overview; Local BRDF Reconstruction

Representative Projection and Bootstrapping3.2.3 Manifold Bootstrapping Details; Estimating Local BRDF Dimensionality; Uniform Measurement Scaling; Neighborhood Selection; Local Linear Combination; 3.2.4 Synthetic Enlargement for Representatives; 3.2.5 Key Measurement Validation; 3.3 SVBRDF Data Acquisition; 3.3.1 Acquiring Representatives: BRDF Samples; Device Setup; Calibration; Capturing; Reconstruction; 3.3.2 Acquiring Keys: Reflectance Maps; Key Lighting Dimensionality; Key Lighting Size; 3.4 Experimental Results; 3.4.1 Method Validation; Test on Fully-Sampled SVBRDF

Comparison with Microfacet SynthesisEffect of Neighborhood Size; 3.4.2 SVBRDF Capture Results; 3.5 Conclusion; References; Chapter 4: Interactive SVBRDF Modeling from a Single Image; 4.1 Related Work; 4.2 System Overview; 4.3 User-Assisted Shading Separation; 4.3.1 Separation as Optimization; 4.3.2 Interactive Refinement; User Strokes; Result Refinement; 4.3.3 Discussion; 4.4 Two-Scale Normal Reconstruction; Discussion; 4.5 User-Assisted Specular Assignment; Material Classification; Specular Coefficient Assignment; 4.6 Experimental Results; Performance; User Input

Comparison with Standard ToolsetsResults; Limitations; 4.7 Conclusion; References; Part II: Modeling and Rendering of Subsurface Light Transport; Chapter 5: Overview of Subsurface Light Transport; 5.1 Computing Subsurface Light Transport; 5.2 Capturing Subsurface Scattering Effects; References; Chapter 6: Modeling Subsurface Light Transport with the Kernel Nyström Method; 6.1 Related Work; 6.2 The Kernel Nyström Method; 6.2.1 Kernel Extension; 6.2.2 Estimating the Light Transport Kernel f; 6.3 Adaptive Light Transport Measurement; Device Setup and Calibration; Column Sampling

6.4 Results and Discussions

A principal aim of computer graphics is to generate images that look as real as photographs. Realistic computer graphics imagery has however proven to be quite challenging to produce, since the appearance of materials arises from complicated physical processes that are difficult to analytically model and simulate, and image-based modeling of real material samples is often impractical due to the high-dimensional space of appearance data that needs to be acquired.This book presents a general framework based on the inherent coherency in the appearance data of materials to make image-based appeara

Description based upon print version of record.

Author notes provided by Syndetics

<p>Yue Dong is an associate researcher in the Internet Graphics Group of Microsoft Research Asia, where his work focuses mainly on appearance modeling with data coherency. He received his Ph.D. in Computer Science from Institute for Advanced Study at Tsinghua University in 2011, under the supervision of Professor Heung-Yeung Shum.</p> <p>Stephen Lin joined Microsoft Research Asia in June 2000 and is currently a Senior Researcher in the Internet Graphics group. His research lies in the fields of computer vision and computer graphics, with particular interests in computational photography, image processing, and photometric analysis. He has published over 80 papers and has served as a program co-chair of the International Conference on Computer Vision (ICCV) 2011 and the Pacific-Rim Symposium on Image and Video Technology (PSIVT) 2009. He received a B.S.E. in electrical engineering from Princeton University and a Ph.D. in computer science and engineering from the University of Michigan.</p> <p>Baining Guo is Assistant Managing Director of Microsoft Research Asia, where he also serves as the head of the graphics lab. Prior to joining Microsoft in 1999, Dr. Guo was a senior staff researcher with the Microcomputer Research Labs of Intel Corporation in Santa Clara, California. Dr. Guo received Ph.D. and M.S. from Cornell University and B.S. from Beijing University. Dr. Guo has published extensively in computer graphics and visualization, in the areas of texture and reflectance modeling, texture mapping, translucent surface appearance, real-time rendering, and geometry modeling. He served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics. He is currently on the editorial boards of Computer and Graphics and IEEE Computer Graphics and Applications. He also served on program committees of most major graphics and visualization conferences, including ACM Siggraph, IEEE Visualization, Eurographics Symposium on Rendering, Pacific Graphics, ACM Symposium on Virtual Reality Software and Technology, and ACM Symposium on Solid and Physical Modeling. Dr. Guo has been granted over 30 US patents. Dr. Guo is a fellow of IEEE.</p>

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