I N T R O D U C T I O N T O C O M P U T E R G R A P H I C S Andy van Dam October 21, 2003 Realism...

I N T R O D U C T I O N T O C O M P U T E R G R A P H I C S

Andy van Dam October 21, 2003 Realism 1/42

• These notes have been created and revised each year by many generations of CS123 TAs and by John Hughes and Andy van Dam

• Updated in 2001 and 2002 by John Alex (former 123 TA and Pixarian, now a Ph.D. student at MIT)

• See also Chapter 14 in the book

Realism in Computer Graphics



Roadmap• We tend to mean physical realism

• How much can you deliver?– what medium are you delivering on? (still images, movie/video special effects, VR, etc.)

– how much resources are you willing to spend? (time, money, processing power)

• How much do you want or need?– content

– users

• There are many categories of realism:– geometry and modeling

– rendering

– behavior

– interaction

• And many techniques for achieving varying amounts of realism within each category

• Achieving realism usually requires making trade-offs– realistic in some categories and not in others

– concentrate on the aspects most useful to your application

• When resources run short, use hacks!

Realism in Computer Graphics



• What is “realism”?– King Kong vs. Jurassic Park

– Final Fantasy

• In the early days of computer graphics, focus was primarily directed towards producing still images

• With still images, “realism” typically meant approaching “photorealism.” Goal was to accurately reconstruct a scene at a particular slice of time

• Emphasis was placed on accurately modeling geometry and light reflection properties of surfaces

• With the increasing production of animated graphics—commercials, movies, special effects, cartoons—a new standard of “realism” became important—behavior

• Behavior over time:– character animation

– natural phenomena: cloth, fur, hair, skin, smoke, water, clouds, wind

– Newtonian physics: things that bump, collide, fall, scatter, bend, shatter, etc.

Realism and Media (1/2)



Real-time vs. Non-real-time• “Realistic” static images and animations are usually rendered in batch,

and viewed later. They can often take hours per frame to produce. Time is a relatively unlimited resource

• In contrast, other apps emphasize real-time output:– graphics workstations: data visualization, 3D design ≈10Hz

– video games ≈60Hz

– virtual reality ≈10-60Hz

• Real-time requirements drastically reduce time available for geometric complexity, behavior simulation, rendering, etc.

• Additionally, any media that involves user interaction (e.g., all of the above) also requires real-time interaction handling

Realism and Media (2/2)



Cost vs. Quality• Many computer graphics media (e.g., film vs. video vs. CRT)

• Many categories of realism to attend to (far from exhaustive):– geometry

– behavior

– rendering

– interaction

• In a worst-case scenario (e.g., VR), we have to attend to all of these categories within an extremely limited time-budget

• The optimal balance of techniques for achieving “realism” depends a great deal on context of use:

– medium

– user

– content

– resources (especially hardware)

• We will elaborate on these four points next…

Trade-offs (1/4)



• Medium– as said before, different media have different needs– consider a doctor examining patient’s x-rays– if the doctor is examining static transparencies, resolution and

accuracy matter most– if the same doctor is interactively browsing a 3D dataset of the

patient’s body online, she may be willing to sacrifice resolution or accuracy for faster navigation and the ability to zoom in at higher resolution on regions of interest

• User– expert vs. novice users– data visualization: novice may see a clip of data visualization on the

news, doesn’t care about fine detail (e.g., weather maps)– in contrast, expert at workstation will examine details much more

closely and stumble over artifacts and small errors—“expertise” involves acute sensitivity to small fluctuations in data, anomalies, patterns, features

– in general, “what does the user care (most) about?”

Trade-offs (2/4)



• Content– movie special-effects pack as much astonishment as possible into their

budget: use every trick in the book– conversely, CAD model rendering typically elides detail for clarity, and fancy

effects only interfere with communication– Scientific visualizations show artifacts and holes in the data, don’t smooth

them out. Also, don’t introduce artifacts due to geometric or rendering approximations (e.g., contouring)

• Resources– you settle for what you can get:– Intel 286 (1989): wireframe bounding boxes– Microsoft Xbox (Nov. 2001): complete computer with

graphics more powerful than a GeForce 3 forless than $200!

– nVidia GeForce FX5900 (2003) texture-mapped, environment-mapped, bump-mapped, shadow-mapped, high- polygon, articulated, physically-simulated,stencil-shadowed goodnessfor $450 fully loaded

Trade-offs (3/4)


Computing to a time budget (“time-critical” algos)• A vast array of techniques have been developed for generating “realistic”

geometry, behavior, rendering…

• The “best” can often be traded for the “good” at a much lower computational price

• We call bargain-basement deals “hacks”

• Some techniques use progressive refinement (or its inverse, graceful degradation): the more time we spend, the better output we get. Excellent for situations when we want the best quality output we can get for a fixed period of time, but we can’t overshoot our time limit (e.g., VR surgery!). Maintaining constant update rates is a form of guaranteed “Quality of Service” (a networking term).

– web image downloads

– progressive refinement for extremely large meshes


Trade-offs (4/4)



• Texture-Maps: map an image onto surface geometry to create the appearance of fine surface detail. A high level of realism may require many layers of textures.

• Environment-Maps: multiple images (textures) which record the global reflection and lighting on a object. These images are resampled during rendering to extract view- specific information which is then applied as a texture to the object.

• Bump-Maps: fake surface normals by applying a height field (intensities in the map indicate height above surface). From height field calculate gradient across surface and use this to perturb the surface normal.

• Shadow-Maps: generate shadow texture by taking silhouettes of objects as seen from the light source. Project texture onto scene from light source. Note: must be recalculated for moving lights.

Digression - Definitions



• The Hacked– Texture mapping: excellent way to fake fine surface detail—more often used

to fake geometry than to add pretty colors

– more complicated texture mapping strategies such as polynomial texture maps use image-based rendering techniques for added realism

Techniques—Geometry (1/4)

• The Good– Polygonization: very finely

tessellated meshings of curved surfaces

– linear approximation

– massively hardware-accelerated!



The Best• Splines

– no polygons at all! Continuous mathematical surface representations (polynomials)

– 2D and 3D curved surfaces: Non-Uniform Rational B-Splines (NURBS)

– control points– high order polynomials are hard to

work with

• Implicit Surfaces (blobbies)– F(x,y,z) = 0– add, subtract, blend– hard to render, manip




The Best• Subdivision Surfaces

– subdivide triangles into more triangles, moving to a continuous limit surface

– elegantly avoid gapping and tearing between features

– support creases

– allow multi-resolution deformations (editing of lower resolution representation of surface)




• The Gracefully Degraded– Level-of-Detail(LOD): as object gets farther away from viewer, replace it with

a lower-polygon version or lower quality texture map. Discontinuous jumps in model detail

– Mesh decimation: save polygons


Left: 30,392 trianglesRight: 3,774 triangles


Good Hacks• easily implemented in hardware: fast!

• use polygons

• only calculate lighting at polygon vertices, from point lights

• for non-specular (i.e., not perfectly reflective), opaque objects, most light comes directly from the lights (“locally”), and not “globally” from other surfaces in the scene

• local lighting approximations– diffuse Lambertian reflection: only accounts for angle between surface normal and

vectors to the light source.

– fake specular spots on shiny surfaces: Phong lighting

• global lighting approximations– introduce a constant “ambient” lighting term to fake an overall global contribution

– reflection: environment mapping

– shadows: shadow mapping

• polygon interior pixels shaded by simple color interpolation: Gouraud shading– Phong shading: evaluate some lighting functions on a per-pixel basis, using interpolated

surface normal.

Techniques—Rendering (1/9)



An example: Quake III• Few polygons (i.e., low geometric complexity)

• Purely local lighting calculations

• Details created by texturing everything with precomputed texture maps– surface detail

– smoke, contrails, damage and debris

– even the lighting and shadows are done with textures

• Bump mapping on some hardware





The Best• Global illumination: find out where all the light entering a scene comes from,

where and how much it is absorbed, reflected or refracted, and all the places it eventually winds up

• Three methods: Raytracing (specular), Radiosity (diffuse), IBR (avoid geometry)

• Early method: Ray-tracing. Method to avoid forward tracing infinitely many light rays from light sources to eye. Work backwards to do viewer/pixel-centric rendering: shoot viewing rays from viewer’s eyepoint through each pixel into scene, and see what objects they hit. Return color of object struck first. If object is transparent or reflective, recursively cast ray back into scene and add in reflected/refracted color

– Turner Whitted, 1980

– moderately expensive to solve

– “embarrassingly parallel”—canuse parallel computer ornetworked workstations

– models simple lighting equation (e.g., ambient, diffuse and specular) for direct illumination but only perfectly specular reflection for indirect (global) illumination




The Best: Ray Tracing (cont.)• Ray-tracing good for: shiny, reflective, transparent surfaces such as

metal, glass, linoleum. Can produce sharp shadows, lensed caustics (focusing of light due to interaction with curved specular surfaces). As these effects appear relatively infrequently in everyday life, grouped together they often look characteristically “computerish”

• Can do volumetric effects, caustics with straightforward extensions (such as “photon maps”)




The Best: Radiosity (Energy Transport) - Diffuse• Scene-centric rendering. Break scene up into small surface patches and

calculate how much light from each patch contributes to every other patch. Circular problem: some of patch A contributes to patch B, which contributes some back to A, which contributes back to B, etc. Very expensive to solve—iteratively solve system of simultaneous equations

– viewer-independent—batch preprocessing step followed by real-time, view-dependent display




The Best: Radiosity (cont.)• Good for: indirect (soft) lighting, color bleeding, soft shadows, indoor

scenes with matte surfaces. As we live most of our lives inside buildings with indirect lighting and matte surfaces, this technique looks remarkably convincing

• Even better results can be obtained by combining radiosity with ray-tracing

– Various methods for doing this. Looks great! Really expensive!




The Gracefully Degraded Best• Selectively ray-trace. Usually only a few shiny/transparent objects in a

given ray-traced scene. Can perform local lighting equations on matte objects, and only ray-trace the pixels that fall precisely upon the shiny/transparent objects

• Calculate radiosity at vertices of the scene once, and then use this data as the vertex colors for Gouraud shading (only works for diffuse colors in static scenes)




The Real Best: Sampling Realistically• The Kajiya rendering equation (covered in CS224 by Spike) describes

this in exacting detail – very expensive to compute!

• Previous techniques were different approximations to the full rendering equation

• Led to the development of path-tracing: point sampling the full rendering equation

• Eric Veach’s Metropolis Light Transport is a faster way of sampling the full rendering equation (CS224)




Techniques—Rendering (9/9)Side Note—Procedural Shading• Complicated lighting effects can be obtained through use of procedural

shading languages– provides nearly infinite lighting possibilities

– global illumination can be faked with low computational overhead

– but usually requires a skilled artist to get decent images

• Pixar’s Renderman

• Procedural shading is now in hardware– nVidia’s GeForce3+ has programmable vertex and pixel shaders

– Cg – nVidia’s C-like language for coding vertex and pixel shaders – assembly no longer required



A Different Approach:• Image-based rendering (IBR) is only a few years old. Instead of spending

a lot of time and money modeling every object in a complex scene, take a photo of it. You’ll capture both perfectly accurate geometry and lighting with very little overhead

• Analogous to image compositing in 3-D

• Dilemma: how to generate views other than the one photo you took. Various answers.

The Hacked• QuickTimeVR.

– Stitch together multiple photos taken from the same location at different orientations. Produces cylindrical or spherical map which allows generation of arbitrarily oriented views from that one position.

– generating multiple views: discontinuously jump from one precomputed viewpoint to the next. In other words, can’t reconstruct missing (obscured) information

Image-Based Rendering (1/2)



The Best• Plenoptic modeling: using multiple overlapping photos, calculate depth

information from image disparities. Combination of depth info and surface color allows on-the-fly reconstruction of “best guess” intermediary views between the original photo-positions

• Lightfield rendering: sample the path and color of many light rays within a volume (extremely time-consuming pre-processing step!). Then interpolate these sampled rays to place the camera plane anywhere within the volume and quickly generate a view.

– Treats images as 2d slices of a 5d function – position (xyz) and direction (theta, phi on sphere)

– Drawbacks: have to resample for any new geometry.

Image-Based Rendering (2/2)



Stills vs. Animation• At first, computer graphics researchers thought, “If we know how to

make one still frame, then we can make an animation by stringing together a sequence of stills”

• They were wrong. Long, slow process to learn what makes animations look acceptable

• One problem: reappearance of spatial aliasing

• Individual stills may contain aliasing artifacts that aren’t immediately apparent or irritating

– impulse may be to ignore them

• Sequential stills may differ only slightly in camera or object position. However, these slight changes are often enough to displace aliasing artifacts by a distance of a pixel or two between frames

Temporal Aliasing (1/3)



Stills vs. Animation• Moving or flashing pixel artifacts are alarmingly noticeable in

animations. Called the “crawlies”. Edges and lines may ripple, but texture-mapped regions will scintillate like a tin-foil blizzard

• How to fix crawlies: use traditional filtering to get rid of spatial artifacts in individual stills




Motion Blur• Another unforeseen problem in animation: temporal aliasing

• This is like spatial aliasing problem, only over time: if we sample a continuous function (in this case, motion) in too few steps, we lose the continuity of the signal

• Quickly moving objects seem to “jump” around if sampled too infrequently

• One solution: motion blur. Turns out that cameras capture images over a relatively short interval of time (function of shutter speed). For slow moving objects, the shutter interval is sufficiently fast to “freeze” the motion, but for quickly moving objects, the interval is long enough to “smear” the object across the film. This is, in effect, filtering the image over time instead of space

• Motion blur a very important cue to the eye for maintaining illusion of continuous motion

• We can simulate motion blur in rendering by taking the weighted average of series of samples over small time increments




Modeling the way the world moves• Cannot underestimate the importance of behavioral realism

– we are very distracted by unrealistic behavior even if the rendering is realistic

– good behavior is very convincing even when the rendering is unrealistic (e.g., motion capture data animating a stick figure still looks very “real”)

– most sensitive to human behavior – easier to get away with faking ants, toys, monsters, fish etc.

• Hand-made keyframe animations– professional animators often develop an intuition for the behavior of physical

forces that computers spend hours calculating

– “cartoon physics” sometimes more convincing or more appealing than exact, physically-based, computer calculated renderings

– vocabulary of cartoon effects: anticipation, squash, stretch, follow-through, etc.

Techniques—Behavior (1/4)



The Best

• Motion-capture– sample positions and orientations of motion-trackers over time. Trackers usually

attached to joints of human beings performing complex actions. Once captured, motion extremely cheap to play back: no more storage required than a keyframe animation. Irony: one of cheapest methods, but provides excellent results

– usually better than keyframe animations and can be used for a variety of characters with the same joint structure (e.g., Brown → CMU’s Nancy Pollard’s research)

– “motion synthesis”: a recent hot topic – how to make new animations out of the motion capture data that you have.




The Best (cont.)• Physics simulations (kinematics for rigid-body motion, dynamics for F =

ma)– Hugely complex modeling problem– expensive, using space-time constraints, inverse kinematics, Euler and Runge-

Kutta integration of forces, N2-body problems. These can take a long time to solve

– looks fairly convincing…but not quite real (yet)




Techniques—Behavior (4/4)The Gracefully Degraded• Break laws of physics (hopefully imperceptibly)

– Simplify numerical simulation: consider fewer forces, use bounding boxes instead of precise collision detection, etc.

– Decrease number of time steps used for Euler integration



Frame Rate• Video refresh rate is independent of scene update rate (frame rate), should

be >=60Hz to avoid flicker– refresh rate is the number of times per second that a CRT scans across the entire

display surface– includes the vertical retrace time, during which the gun is on its way back up

(and is off)– must swap buffers while gun is on its way back up. Otherwise, get “tearing”

when parts of two different frames show on the screen at the same time– to be constant, frame rate must then be the output refresh rate divided by some

integer (at 60Hz output refresh rate, can only maintain 60, 30, 20, 15, etc. frames per second constantly)

• Frame rate equals number of distinct images (frames) per second

• Best: frame rate is as close to refresh rate as possible

• Best: frame rate is close to constant– humans perceive changes in frame rate (jerkiness) – fundamental precept of “real-time:” know ahead of time (i.e., guarantee) exactly

how long each frame will take– polygonal scan conversion: close to constant, but not boundable, time– raytracing: boundable time, but image quality varies wildly

Real-time Interaction (1/6)



Frame Rate (cont.)• Insufficient update rates can cause temporal aliasing—the breakup of

continuity over time – jerky motion

• Temporal aliasing not only ruins behavioral realism but destroys the illusion of immersion in VR.

• How much temporal aliasing is ‘bad’?– in a CAD-CAM program, a 10 frame-per-second update rate may be

acceptable because the scene is relatively static, usually only the camera is moving

– in video games and simulations involving many quickly moving bodies, a higher update rate is imperative: most games aim for 60 fps but 30 is often acceptable

– motion blur is expensive in real-time graphics because it requires calculation of state and complete update at many points in time




Frame Rate and latency• Frame time is the period over which a frame is displayed (reciprocal of

frame rate)

• Problem with low frame rates is usually “latency,” not smoothness

• Latency (also known as “lag”) in a real-time simulation is the time between an input (provided by the user) and its result

– best: latency should be kept below 10ms or there is noticeable lag between input and result

– noticeable lag affects interaction and task performance, especially for an interactive “loop”

– large lag causes potentially disastrous results; a particularly nasty instance is VR-induced “cyber sickness” which causes fatigue, headaches and even nausea

– lag for proper task performance on non-VR systems should be less than 100ms




Frame Rate and Latency (cont.)• Imagine a user that is constantly feeding inputs to the computer

• Constant inputs are distributed uniformly throughout the frame time, collect and process one (aggregate) input per frame

• Average time between input and next frame is ½ of frame time

• Average latency = ½ frame time– at 30Hz, average latency is 17ms>10ms

– at 60Hz, average latency is 8.3ms<10ms

– therefore, frame rate should be at least 60Hz

• Must sample from input peripherals at a reasonable rate as well– often 10-20 Hz suffices, as the user’s motion takes time to execute

– high-precision and high-risk tasks will of course require more (Phantom (haptic) does 1000 Hz!)

– in CAVE many users prefer 1–2Hz (especially if it has geometrical accuracy) to 5–10Hz; somehow it is less disconcerting




Rendering trade-offs• Frame rate should be at least 60Hz. 30 hurts for very interactive

applications (e.g., video games)– only have 16.7ms (frame time) to render frame, must make tradeoffs

– VR often falls short of this ideal

• What can you get done in 16.7ms?

• Do some work on host (pre-drawing)

• Best: multiprocessor host and graphics cards– accept and integrate inputs throughout frame (1 CPU)

– update database (1+CPUs)• swap in upcoming geometry and texture

• respond to last rendering time (adjust level of detail)

• test for intersections and respond when they occur

• update view parameters and viewing transform

– do coarse view culling, scenegraph optimizing (1 CPU per view/pipe)




Rendering trade-offs (cont.)• Do rest of work (as much as possible!) on specialized graphics hardware• Best (and hacked): multipass

– combine multiple fast, cheap renders into one good-looking one– full-screen anti-aliasing (multi-sampling and T-buffer, which blends multiple

rendered frames)– Quake III uses 10 passes to hack “realistic rendering”

• 1-4 bump mapping• 5-6 diffuse lighting, base texture• 7 specular lighting• 8-9 emissive lighting, volumetric effects• 10 screen flashes (explosions)

• Good lighting and shading – Phong/Blinn lighting and Phong

shading models – tons of texturing, must filter

quickly (mipmaps) and anisotropically (ripmaps)




Improving standards over time

• Bigger view, multiple views– engage peripheral vision

– multiple projectors• caves, spherical, cylindrical and dome screens

• render simultaneously (1+CPUs and graphics pipelines per screen)

• must do distortion correction and edge blending

– stereo rendering (double frame rate)

• We rarely have the patience for last year’s special effects, much less the last decade’s

• The quality of “realism” increases with every new technique invented

• Was “Tron” a convincing virtual reality?

• “Lord of the Rings” looks realistic now, but for how long?

• What will realism look like next year? That’s for all you Sceneview hackers to decide…

Raising the Bar



One last digression• Artistic rendering—trying to evoke hand-drawn or hand-painted styles,

such as charcoal sketching, pen and ink illustration, or oil painting

• For certain applications, elision of some details and exaggeration of others can be helpful (mechanical illustration, scientific visualization, etc.)

• Non-realism is also used in behavior (cartoon physics), interaction (paintbrushes, virtual tricorder and other virtual widgets in VR), geometry (Monsters, Inc.)

• Examples of non-realism:– Finding Nemo

– Graphics Lab’s SKETCH system emphasizes simplified geometry as well as simplified rendering

– some research has investigated “cartoon physics” and other kinds of exaggerated motion and behavior

• Strategic use of non-realism is a brand new field with many opportunities

Non-Photorealistic rendering



Examples

• Cartoon Shading – you’ll be doing this in modeler!

• WYSIWYG NPR – Draw strokes right onto 3d models, paper by Spike and other Brownies!

• NPR allows for more expressive rendering than traditional lighting

Non-Photorealistic rendering



Important recent SIGGRAPH papers!• Brown and it graduates have become identified with some of the hottest

research in non-realistic rendering: David Salesin, Cassidy Curtis, Barbara Meier, David Laidlaw, Spike, and Lee Markosian are all pioneers in the field.

Brownies and NPR



Brown’s Expertise in Realism

• Geometric modeling (Spike et al.)

• Animation (Barb Meier)

• Rendering for VR (David et al.)

• Interaction for VR (Andy et al.)

• NPR (Spike et al.)

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