I hope that you: Looked at book & website Checked Pre-requisites (change before Friday!) Participate! Ask Questions! Get Inspired … CS395: Advanced Computer Graphics week 1: ‘5 Minute Madness’ Ben Watson Jack Tumblin

Image-Space Techniques ‘Image’==A 2D map of light intensities from a lens‘Image’==A 2D map of light intensities from a lens ‘Digital Image’==a 2D grid of numbers (pixels)‘Digital Image’==a 2D grid of numbers (pixels) PROBLEMS:PROBLEMS: –What is between all those points? –Why go digital? Why is it better than film? –How can I combine the best of many pictures? Edit easily? GOAL: –More Flexibility; let me do more than just display!

Image-Space Techniques GOAL: More flexibility! Compositing /Matte: cut-and-paste, transparency, the ‘digital optical bench’ (Pixar`84)…Compositing /Matte: cut-and-paste, transparency, the ‘digital optical bench’ (Pixar`84)… Warp: image as a ‘rubber sheet’, you can cut, stretch, and change at willWarp: image as a ‘rubber sheet’, you can cut, stretch, and change at will Environment Matte (Salesin99…)Environment Matte (Salesin99…) Video Textures(Schodl 2000)Video Textures(Schodl 2000) Polynomial Texture Map (Malzburg2001)Polynomial Texture Map (Malzburg2001)

Global Illumination Or ‘Physically Accurate Rendering’ PROBLEMS:PROBLEMS: –‘CG looks a bit weird, & needs lots of tweaking… –No Predictive Ability: can’t compute light meter #s GOALS:GOALS: –‘Pixel Drawing’  Rendering from 1 st principles; –Science –Science: fundamental optical principles, invariants

Global Illumination ??Basic Physics!? Why isn’t this solved already? –Endlessly Bouncing Light: Radiance Field is 5-D The Rendering Equation (+ the math makes it look more scary than it is) –REAL Materials are Interesting, largely unmeasured BRDF alone is 4D, ‘spiky’, interesting But Light scatters WITHIN materials too! –Computational Complexity—cleverness wanted… (naïve solutions are exponential time: O(e N ))

Global Illumination But real surfaces are more complicated! Specular efx., etc.

Global Illumination Seminal Papers:Seminal Papers: Kajiya, Goral, Hanrahan, Cohen, Rushmeier, … Excellent Freeware Solution:Excellent Freeware Solution: Greg Wards’ RADIANCE (1 st sub-area to struggle for relevance(1 st sub-area to struggle for relevance –(Why bother when light maps/ multitexturing on my $200 video card looks great in real time?) Opinion: soon other areas will hit this scrutiny)Opinion: soon other areas will hit this scrutiny)

Light Fields & Approximations An Inspired Observation: (at an informal ‘bull session’ at SIGGRAPH`94: Levoy, Hanrahan, Rushmeier, Cohen, others…) Idealized ‘Bubble of Cameras’ records all raysIdealized ‘Bubble of Cameras’ records all rays Any image outside bubble == subset of raysAny image outside bubble == subset of rays … …

Light Fields & Approximations.

PROBLEM: –Prompt rendering  dull, inaccurate result. –Poor tradeoff of interaction vs. rendering realism. Can’t we do more than movies?GOAL: –Capture ALL the light leaving an object –Fast sort, re-display allows interaction. Light Fields & Approximations

‘Lumigraph’ or ‘Light Field’‘Lumigraph’ or ‘Light Field’ –Limited, practical subset of this 5-D ray set –2-plane camera: easy way to organize the rays. –Light Field cameras possible (but tough/expensive) –Storage efficiency is low—compression needed! –‘Output only’; can’t change lighting effects –Tangles together light & surface efx. Light Fields & Approximations

Recent: Try to separate illumination from surface properties in the light-field data. Univ. of Washington SIGGRAPH 2000

Image-Based Modeling & Rendering PROBLEM: Geometric modeling is tedious, slow, expensive Most modeled objects exist already; 3D laser scanning, etc. is expensive, finicky, difficult Geom. Models almost never look as good as photos Meanwhile: prices fall, speed & capacity rise for Digital Cameras, Displays, Memory, Desktop PCs… !Lets use cameras to eliminate the tedium!

Image-Based Modeling & Rendering GOAL 1: Generalize Photography:GOAL 1: Generalize Photography: –Get MORE than light; –Estimate shape, texture, reflectance, lighting, … –A Re-thinking of pointwise 3-D scanning… GOAL 2: Generalize Image Viewing:GOAL 2: Generalize Image Viewing: –Interactive, movable viewpoint –‘Reprojection: re-use already-rendered image parts

Image-Based Modeling & Rendering Seminal Papers:Seminal Papers: –McMillan`95: Plenoptic Function… –Kanade –View-based Reconstruction… –LDI Trees –Image-Based Visual Hulls (Gortler,McMillan…)