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Interactive photo-realistic 3D digital prototyping

Mastering real-world materials using bidirectional texture functions. OREALIA ™. Interactive photo-realistic 3D digital prototyping. Luc Claustres – Technical Director. - Presentation.

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Interactive photo-realistic 3D digital prototyping

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  1. Mastering real-world materialsusingbidirectional texture functions OREALIA™ Interactive photo-realistic 3D digital prototyping Luc Claustres – Technical Director

  2. - Presentation • Onesia develops and markets real time interactive photorealistic 3D rendering for computer-aided design, scientific visualization, and industries that require virtual prototyping • Orealia™ is the first entry-level virtual prototyping solution offering the power of high-end 3D rendering softwares

  3. - Adressedproblems • Product design is too slow • reduce time to market • Costs of Physical prototypes are too high • no longer required • The interaction between Production and Marketing departments is difficult • user-friendly interface to share data • The quality of the output is not realistic • enough to be used “out of the box”

  4. - Workflow DESIGN MARKET Lighting Materials Variances Interactivity IMPORT OPTIMIZE Catia Pro/Engineer SolidWorks Rhino 3D Google SketchUp … DEPLOY SHARE Stand-aloneApp Web 2.0 Image/Video Virtual Reality  

  5. - BTF (Bidirectional Texture Function) • Goal : capture the appearance of real-world materials and apply it to virtual prototypes in real-time • Single texture (photograph) • simple but unrealistic : flat appearance • Textures for different lighting/viewing configurations • realistic : reflection, anisotropy and self-shadowing

  6. - BTF overview CPU Compression Optimization Acquisition 4 Textures Light direction Viewer direction GPU BTF’s Evaluation Vertex/Pixel Shader Real-time 3D Visualization

  7. - BTF definition • Light-Matter interaction is quite complex ! • function defining how light is reflected on a surface • We need to simplify • ti = tr, λi = λr, xi = xr→ BTF • if invariant with respect to x (plain color) →BRDF

  8. - Acquisition • Measurement device example • 151 digital consumer cameras (Canon PowerShot A75) • 2/3 measurements per day • post-processing required for shiny materials like paint • Generates a huge amount of data • 81x81 lighting/viewing directions – 256x256 = 1,3 GB

  9. - Modelling • BTF viewed as a spatial-varying BRDF [McAlliste et al.] • For each pixel we use the Lafortune’s model • Generalise the Phong’s model • Non-linear • Anisotropic • Adapted to a wide range of materials

  10. - Compression • Parameter estimation • Diffuse component : ρd = minimum of all values • Albedo simplification : ρsiscommon to all lobes • Lobes : find best Cx,j, Cy,j, Cz,j, nj(j < 3) • Levenberg-Marquardt non-linear optimisation • Residual s(x) to model the error • least square approximation of BTF(x, ωi, ωr) - fr,x(ωi, ωr)

  11. - Reconstruction • Performed through a vertex/fragment shader • Retrieve texture coordinates • Filter parameters stored in textures accordingly • Compute light/viewing configuration • Evaluate Lafortune’s model + residual accordingly • Evaluate the rendering equation (sum up light contributions)

  12. - BTF pipeline summary CPU (analysis) GPU (synthesis)

  13. - Results (error)

  14. - Results (speed)

  15. - Results (demo) • This workisbased on a technologicaltransfer • partnershipwith IRIT/UPS/CNRS (Toulouse - France) • This workused public data • provided by the GraphicsLabat Bonn University

  16. THANKS FOR YOUR ATTENTION You are welcome at ONESIA booth B8

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