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Statistical and numerical techniques for photorealistic image synthesis. Kartic Subr. Who am I?. Born in India Bangalore University (Bachelor of Engineering) 2001 Hewlett Packard, India/Singapore 6 years in USA PhD , University of California Irvine, 2008
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Statistical and numerical techniquesfor photorealistic image synthesis Kartic Subr
Who am I? • Born in India • Bangalore University (Bachelor of Engineering) 2001 • Hewlett Packard, India/Singapore • 6 years in USA • PhD, University of California Irvine, 2008 • Advisor: Jim Arvo (PhD Yale University), pioneered methods in light transport • 2 years in France • Post doctoral researcher, ARTIS, INRIA-Grenoble (2008-2010)
My goal: Generating realistic visuals Gustave Courbet, Stone-Breakers, 1849. Realism in art Wilhelm Oswald Gustav Achenbach, Abendstimmung in der Campagna, 1850.
My goal: Generating realistic visuals Photograph: Nicéphore Niépce, 1826 Gustave Courbet, Stone-Breakers, 1849. Realism in art Wilhelm Oswald Gustav Achenbach, Abendstimmung in der Campagna, 1850.
Notion of “realism” depends on technology Pedro Campos Gerhard Richter, 1983 Hyperrealism
Image ? Image synthesis involves light transport Light sources Digital models of scene (geometry + materials) Virtual camera
Image synthesis adds visual impact Digital model Captured video + digital model Avatar
Applications of image synthesis Entertainment Advertising Virtual prototyping Defense Biomedical imaging
Multidisciplinary nature of the problem • Physically based optical simulations • Mathematical tools for analysis • Numerical techniques for light transport solution • Understanding biological processes eg. Perception et cognition
Light transport: multi-domain integration • Combinatorial explosion from sampling each domain Exposure time Image space Aperture Visible spectrum Reflectance Direct illumination Indirect illumination [Efficient sampling strategies for Monte Carlo integration (my PhD thesis)]
Talk outline • Recent contributions • Simulating defocus • Rendering translucent materials • Research plan • Core problems in image synthesis • Model representation and abstraction
Defocus is due to aperture integraion Lens Aperture Image Pixel p
Defocus Pixel p Lens Aperture Scene Image Pixel p
Monte Carlo estimation of aperture integral NA primary rays per pixel Aperture Image Integrate at p
Aperture integration is very costly NP x NA Primary rays Image Aperture NP pixels NA Aperture samples
Paradox: Blurry image is costlier to compute! 64 x #primary rays of the pinhole image
Observation 1: Image Blurry regions should not require dense sampling of the image
Observation 2: Lens Regions in focus should notrequire profuse sampling of the lens for diffuse objects
Fourier depth of field • Fourier domain analysis of finite aperture cameras • Adaptive sampling • Speedup of around 20 over existing algorithms [ACM Transactions on Graphics 2009. Presented at ACM SIGGRAPH 09] Collaborators: MIT
Translucency: Sub-surface scattering Opaque Translucent • Brute force Monte Carlo: prohibitively expensive • Diffusion approximations: severe constraints on geometry
Finite difference method on new domain • Approximation: diffusion equation • Domain: Dual graph of tetrahedralization Diffuse flux
Rendering translucent materials • Arbitrary geometry • Heterogenous materials • Dynamically deforming shapes • In real-time! [Computer Graphics Forum 2010. To be presented at Eurographics 2010] [Collaborators: Microsoft Research, Tsinghua University]
Research program Model representation and abstraction Realistic image synthesis
1. Realistic image synthesis • Bandwidth driven sampling • Transport of local light field spectrum • Derive spatial / angular sampling rates • co-advising PhD student Laurent Belcour (ARTIS) • Importance vs radiance • Tracing from eye vs tracing from light • Monte Carlo matrix chain multiplication Short-term Long-term
Importance vs radiance Radiance
Importance vs radiance Importance
MC matrix-chain product estimator Related to optimal matrix chain multiplication using dynamic programming?
2. Model representation and abstraction • Abstracting detail in geometry • First step: images (published at SIGGRAPH Asia 09) • Alternate representation • Voxel data to represent geometry and materials Short-term Long-term
Detail = oscillations between extrema Local maxima Input Local minima
Image multiscale decomposition 1D Intensity Input Fine + Medium + Coarse Pixels
Allows smoothing high-contrast detail Input Smoothed
Thank you! • Collaborators • Established • MIT, USA • Microsoft Research • Tsinghua University, China • University of California, Irvine • Current • Cornell University, USA • University of California, Berkeley • Potential • Indian Institute of Information Technology • International journal publications • Computer Graphics Forum 2010: Translucent materials. 4th author of 6 • TOG 2009: Multiscale image decomposition. 1st author of 3 • TOG 2009: Fourier Depth of Field. 2nd author out of 5 • Refereed international conference papers • Pacific Graphics 2007: Statistical hypotheses. 1st author of 2 • Interactive raytracing 2007: Steerable importance sampling. 1st author of 2 • ICIAP 2005: Contrast enhancement. 1st author of 3
Merci ! • Collaborators • Established • MIT, USA • Microsoft Research • Tsinghua University, China • University of California, Irvine • LJK Grenoble • Current • Cornell University, USA • University of California, Berkeley • Potential • Indian Institute of Information Technology • International journal publications • Computer Graphics Forum 2010: Translucent materials. 4th author of 6 • TOG 2009: Multiscale image decomposition. 1st author of 3 • TOG 2009: Fourier Depth of Field. 2nd author out of 5 • Refereed international conference papers • Pacific Graphics 2007: Statistical hypotheses. 1st author of 2 • Interactive raytracing 2007: Steerable importance sampling. 1st author of 2 • ICIAP 2005: Contrast enhancement. 1st author of 3 • Teaching • Columbia University, USA (120 h) • University of California, Irvine (360 h) • Industry • Rhythm and Hues Studios • NVIDIA Corporation • Hewlett Packard