CS395/495: Advanced Computer Graphics
Overview—Eight Big Ideas
Jack Tumblin
[email protected]
I hope that you:
•Looked at book & website
•Want to stay in this class (change before Friday!)
•Participate! Ask Questions! Get Inspired …
8 Topics: You Choose 2
• Class Presentation on a selected paper
– Must choose topic area by Monday
– 1-3 brave volunteers needed for next week
• Write Broad Survey Paper --OR-Write, Demo Programming Project
– Feb 4: reference list due (survey paper) –or-1-2 page proposal due (projects)
– Mar 13: Paper or Project Demo due.
• CS495: (Graduate) two topic surveys
– Feb 18: 1st survey paper due
– Mar 13: 2nd survey (or project) due
8 Topics
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
#1
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Shape representation
The problem:
– We need to make pictures of things.
– What data structures describe shape?
(some are tough for polygons—hair,
smoke, water, cloth, peach, fuzz,
The solutions…
Parametric surfaces
The idea:
– The scene is a collection of patches.
– Patch = curved, continuous. polynomial of u,v
• parameters (u,v) vary across surface
• point on surface: (x(u,v), y(u,v), z(u,v))
– Manipulate the patches w/ control pts.
Critique:
– Nice user control, no faceting, succinct.
– Accurate rendering was slow (less true now)
– LOD control is tough...
Parametrics:
Utah, Erlangen, Pixar
Points
The idea:
– A whole bunch of 3D points only
**No connectivity, no edges, no faces**
– Each with associated colors, normals.
– We fill in the gaps with some “blurring”.
Critique:
– With hierarchy, can render big things.
– What about surface mappings?
Points: QSplat
Stanford: Rusinkiewicz & Levoy
Volumes
The idea:
– The scene is a 3D image.
– Pixels become voxels.
Critique:
– Nice for medical data, transparency.
– Data size huge: 1003 = one million!
Volumes: Univ. Stuttgart
Many Others…
Implicit surfaces
Nice for fluids, topology-changing
Constructive solid geometry
Used in engineering: cut(), union(), diff()...
Procedural modeling
shape is result of a computed function
Natural things: clouds, fire, plants
#2
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Image-Space Techniques
• ‘Image’==A 2D map of light intensities from a lens
• ‘Digital Image’==a 2D grid of numbers (pixels)
• PROBLEMS:
– What is between all those points?
– How can I use images to make other images
GOAL:
– More Flexibility; let me do more than just display!
‘Digital’ Images: 2D Grid of Numbers
• NO intrinsic meaning, but ...
• Widely assumed to represent
(!weasel-word!)
– Point Samples of a “smoothed” 2D intensity surface
– Uniform sampling pattern (but not always)
Image-Space Techniques
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GOAL: More flexibility!
Compositing /Matte: cut-and-paste,
transparency , the ‘digital optical bench’ …
Warp: image as a ‘rubber sheet’, you can
cut, stretch, and change at will
Compositing / Environment Matte
(Pixar`84) (Salesin99…)
Texture Synthesis/elaboration
Video Texture(Schodl 2000)
Polynomial Texture Map (Malzburg2001)
Image-Space Techniques
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GOAL: More flexibility!
Compositing / Environment Matte
(Pixar`84) (Salesin99…)
Image-Space Techniques
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GOAL: More flexibility!
Texture Synthesis/elaboration
Efros98, Wei/Levoy99, Ashikhmin2000,
Video Texture(Schodl 2000),
Input Sample
Synthesized Result
Image-Space Techniques
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Warp: image as a ‘rubber sheet’, you can cut, stretch,
and change at will
http://www.cs.rochester.edu/
u/kyros/Courses/
ECE102/lecture-3-web/sld018.htm
.http://www.cs.uu.nl/~martijn/Applets/warp.html
#3
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Surface mappings
The problem:
– We need more detail on our shapes.
– But our shapes are already complex.
The solutions…
Procedural mappings
The idea:
– Like texture mapping, except:
– The RGB value is f(u,v), not an image.
Critique:
– Very succinct, new possibilities.
– Until recently, no hardware support.
– Hard to “model” procedures.
Procedural mappings: Perlin
Bump mapping
The idea:
– A kind of procedural mapping
– Adds fine “bumpiness” to the surface
Critique:
– Works as advertised.
– Until recently, no hardware support.
Bump mapping: NVidia
Many Others…
Environment mapping
Nice for mirrored surfaces
Light mapping
Cheat to get global illumination
Displacement Maps
Add little bits of geometry to surface
#4
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Global Illumination
Or ‘Physically Accurate Rendering’
• PROBLEM:
– ‘CG looks a bit weird, & needs lots of
tweaking…
– Not Predictive: can’t compute light meter #s
• Solutions:
– ‘Pixel Drawing’  Rendering from 1st principles;
– Science:
fundamental optical principles, invariants
– Serious Math & Algorithms work...
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 complex,
and largely unmeasured
• BRDF alone is 4D, ‘spiky’, interesting
• But Light scatters WITHIN materials too!
– Computational Complexity—cleverness req’d…
(naïve solutions are exponential time: O(eN))
Global Illumination
Surface Properties:
Why Phong shading is
not enough:
Real surfaces are much
more complicated!
Specular effects,
self-shadowing, refraction,
interreflection, BRDF...
Global Illumination
• Seminal Papers:
Kajiya, Goral, Hanrahan, Cohen, Rushmeier, …
• Excellent Freeware Solution:
Greg Wards’ RADIANCE
Aside
• 1st graphics 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 tough scrutiny
#5
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Shape manipulation
The problem:
– Want adjust our shapes;
wiggle, twist, simplify. BUT
– Don’t want to redo all our modeling again.
The solutions…
Model simplification
The idea:
– It should look the same, but…
– It should have fewer primitives.
Critique:
– In general, it works!
– Only limited user control.
– Surface mappings poorly handled.
Simplification: Watson
Surface fitting
Surface fitting: Find surface from a cloud of points
Example
Hoppe ‘94
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13,000 points
3
2
3D Morphing
Change shape A to shape B (Turk/O’Brien99)
#6
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
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
An Inspired Observation: (at an informal ‘bull session’
at SIGGRAPH`94: Levoy, Hanrahan, Rushmeier, Cohen, others…)
• ‘Sphere of Cameras’ records all rays
• Any image outside bubble == subset of rays
…
‘Scene’
causes
Light Field
Light
Fields
& Approximations
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Light field: holds all outgoing light rays
Shape,
Position,
Movement,
Emitted
Light
BRDF,
Texture,
Scattering
Reflected,
Scattered,
Light …
…
Scene modulates
outgoing light;
light field
captures it all.
Light Fields &
Approximations
.http://graphics.stanford.edu/projects/lightfield/
http://www.intel.com/research/mrl/research/lfm/
Light Fields & Approximations
Recent:
Try to separate illumination from surface
properties in the light-field data.
Univ. of Washington SIGGRAPH 2000
#7
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Non-photorealism
The problem:
– Reality / Photos are too narrow
• skips drawing, painting, illustrations, etc.
– Let’s make broader classes of
artificial renderings!
The solutions…
Painterly approaches
The idea:
– Make the image look like it’s painted.
Critique:
– Works well from image or model.
– What about Picasso?
– How interactive is it?
Painterly NPR
Sketching approaches
The idea:
– Make the image look like it’s drawn.
Critique:
– Works well from image or model.
– Can we add imperfection?
Sketching NPR
Toon approaches
The idea:
– Make the image look like it’s a cartoon.
Critique:
– For games, hardware support is key.
– Is it there?
Toon NPR
#8
SCHEDULED ORDER STILL UNKNOWN!
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Shape Representation
2D Image-Space Techniques
Surface Mappings
Global Illumination and Light Transport
Shape Manipulation
Light Fields and Approximations
Non-Photorealistic Rendering (NPR)
Model Acquisition and IBMR
Model Acquisition & IBMR
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 !
Model Acquisition & IBMR
Panorama Stitching...
Model Acquisition & IBMR
How does photo change
with lighting direction?
(Malzbender,2000: Polynomial Texture Maps
))
Model Acquisition & IBMR
How does photo change with lighting direction?
(Malzbender,2000: Polynomial Texture Maps)
Ordinary
Photo
Computed
Specular
Highlight
PTM
Reconstruction
PTM +
Highlight
Model Acquisition & IBMR
• 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:
– Interactive, movable viewpoint
– ‘Reprojection: re-use already-rendered image
parts