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Accurate Appearance Preserving Prefiltering for Rendering - - PowerPoint PPT Presentation

Mar 03, 2023 395 likes •911 views

Accurate Appearance Preserving Prefiltering for Rendering Displacement-Mapped Surfaces Lifan Wu 1 Shuang Zhao 2 Ling-Qi Yan 3 Ravi Ramamoorthi 1 1 University of California, San Diego 2 University of California, Irvine 3 University of California,

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Accurate Appearance Preserving Prefiltering for Rendering Displacement-Mapped Surfaces

Lifan Wu1 Shuang Zhao2 Ling-Qi Yan3 Ravi Ramamoorthi1

1University of California, San Diego 2University of California, Irvine 3University of California, Santa Barbara

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Realistic Appearance Models

Simple surface Complex surface

Image courtesy of Mitsuba [Jakob 2010]

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Appearance Models with Rich Details

[Zhao et al. 2011] [Jakob et al. 2010] [Heitz et al. 2015] [Khungurn et al. 2015] [Wu et al. 2011] [Han et al. 2007] [Yan et al. 2014, 2016]

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Modeling Details

+ Base shape Micro-geometry Micro-scattering

Normal map (2D) Disp. map (2D) Volume (3D) BRDF (Hemispherical) Phase function (Spherical)

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Problems

Complex light-surface interaction ray Micro-geometry Difficult to compute and analyze

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Motivation

  • Camera zooming out ➔ less details are visible ➔ use coarser models

[Zhao et al. 2016]

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  • Prefilter high-resolution displacement maps + BRDFs
  • Preserve appearance

Our Goal

≈

Original Prefiltered

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Prefiltering

Close-up views Distant views

More and more details are aggregated

Coarser models representing aggregate micro-appearance Match the desired appearance Precomputed before rendering

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  • Anti-aliasing, storage reduction

Benefits

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Challenges

  • Difficult to accurately capture changes of illumination effects
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Our Contributions

Anti- aliased Accurate General surface

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Background

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2D Displacement Maps

  • Describe surface

details (micro-geometry)

  • Need expensive

super-sampling Close-up views Distant views

base surface patch actual surface

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Displacement mapping

base surface patch

  • Surface patch
  • Micro-geometry
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Prefiltering

  • Jointly handle changes of illumination effects
  • It is challenging due to non-linearity
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Previous Work

[Han et al. 2007] Normal variation [Wu et al. 2011] Normal variation + Shadowing-masking [Iwasaki et al. 2012] Normal variation + Shadowing-masking

  • Handle parts of illumination effects
  • Missing
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  • Assuming certain types of surface (Gaussian/GGX/V-groove)
  • Fail to generalize

Previous Work

[Dupuy et al. 2013]

Gaussian surfaces General surfaces

[Olano and Baker 2010] [Heitz et al. 2016] [Lee et al. 2018] [Xie and Hanrahan 2018] different

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Previous Work

  • Iterative inverse rendering (optimization) is expensive
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Our Approach vs. Previous Work

Method Interreflections General surfaces Precomputation Bi-Scale No Yes Fast Microfacet Yes No Very fast Inverse

  • ptimization

Yes Yes Slow Ours Yes Yes Fast

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Effective BRDF

Micro-geometry Micro-BRDF Effective BRDF

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  • Weighted average BRDF over

Effective BRDF

cos term shadowing micro-BRDF weighted by visible projected area normalization term [Wu et al. 2011] [Dupuy et al. 2013]

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Our Approach

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Overview

≈ ≈

Before prefiltering After prefiltering

Joint prefiltering Appearance matching LoD rendering

  • Joint prefiltering
  • Appearance matching
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Effective BRDF with Interreflections

Without interreflections With interreflections

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Effective BRDF with Interreflections

Multi-bounce path integral Single-bounce contribution

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Overview

Joint prefiltering Appearance matching LoD rendering

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Downsampling Displacement Maps

Matching meso-normals High-resolution disp. map Low-resolution disp. map

  • Solved using least-squares
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Overview

Joint prefiltering Appearance matching LoD rendering

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Step 1: Multi-Lobe SVBRDF

  • NDF: A (hemi-)spherical distribution of normal directions
  • Statistical representation: decorrelating positions and normals

Micro-geometry NDF Multi-lobe NDF

Image courtesy of [Heitz 2014]

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  • Normal mapping [Han et al. 2007]
  • Multi-lobe BRDF = Multi-lobe NDF Micro-BRDF

Step 1: Multi-Lobe SVBRDF

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Overview

Joint prefiltering Appearance matching LoD rendering

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Step 2: Scaling Function

  • Matching effective BRDFs
  • Computing the scaling function directly:
  • No need for iterative optimization
  • Not a practical algorithm
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Overview

Joint prefiltering Appearance matching LoD rendering

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Efficient Factorization

  • Impractical to compute and store the full 6D scaling function
  • Rank-1 factorization
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  • and can be tabulated coarsely (42 and 154)
  • They can be reconstructed from sparse 6D samples

Efficient Factorization

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Efficient Factorization

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Single Scale

Joint prefiltering Appearance matching LoD rendering

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  • Prefilter at each mipmap level
  • Interpolate path contributions traced on different levels

Multi-Scale LoD

Joint prefiltering Appearance matching LoD rendering

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Results

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  • Determine angular resolutions ( : 152, : 152)

Scaling Function Resolution

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  • Determine spatial resolutions (uv: 42)

Scaling Function Resolution

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  • Energy conservation
  • Synthetic two-color

V-grooves

Validations

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Accuracy Comparison

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LoD Rendering

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Changing Lighting/Viewing

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  • Fail when the vertical displacements are large
  • Rely on model-dependent precomputation
  • Theoretical analysis of appearance prefiltering
  • Material editing

Limitation / Future Work

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Future Work

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  • Machine learning + appearance modeling
  • Next talk!
  • Neural BTF Compression and Interpolation [Rainer et al. 2019]
  • Unified Neural Encoding of BTFs [Rainer et al. 2020]
  • …

Future Work

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Conclusion

Anti- aliased Accurate General surface

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Thank you!