# Shape Representation Basic problem We make pictures of things How do we describe those things? Many of those things are shapes Other things include

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1 Shape Representation Basic problem We make pictures of things How do we describe those things? Many of those things are shapes Other things include motion, behavior Graphics is a form of simulation and modeling Two general types of representations Surface representations Concerned only with the surface of the shape, not the interior Generally more efficient at representing surface Polygons are one example Volume representations Also concerned with interiors, surfaces can be extracted Surface reps (Boundary reps or B-reps ) Parametric surfaces Basic idea Define curves and surfaces using parametric formula Allow modelers to manipulate shape with "control points" Use parametric representations Q(t) = T M G, where T = [t^3 t^2 t 1] Segment runs from 0 to 1 in t M = basis matrix (4x4) G = geometry matrix (4x4) Geometry matrix Contains four geometry elements (pts, vectors) These are the controls the user manipulates in modeling curves interpolate or bend towards them Every differently shaped curve will have a different geometry matrix Basis matrix Describes relative weighting of geometry as t varies Described by the blending function Different curve types (see below) have different bases Basis is constant for a given curve type Curve issues Locality If I change a geometry element, how much of the curve changes? Joining For complex curves/shapes, must join two or more curves together Would like join to be continuous ( smooth ) G0, G1, C1, C2 continuity Effect of transformations Invariance: Transform(Q(t)) = T M transform(g)? Affine invariance Under scales, translations, rotations Perspective invariance 2D curves from projected 3D control points Convex hull property Curve lies inside convex hull of geometry Convex hull: smallest convex polygon (hedron) that contains control points Useful for trivial rejects in clipping, intersection tests

2 Useful for added intuition in modeling Types of curves Bezier Geometry [P1 P2 P3 P4] Four control points Curve interpolates endpoints Slope at ends equals slope of [P2-P1], [P4-P3] Joining G0: overlay endpoints G1: give [P4-P3], [P5-P4] same slope C1: same magnitude C2: hard Locality: none Invariance: affine, not perspective Convex hull property B-Splines Problem: Joining with continuity is difficult There is no C2 continuity Solution: Curves (sequences of segs) are defined by a sequence of n control points Each sequential set of 4 points defines a segment Each new segment shares 3 points with previous segment Knots are the boundaries between each curve segment in t Examples Given a B-spline with n control points There will be n-3 curve segments There will be n-2 knots Given 4 control points one segment two knots (begin and end) Given 10 segments 13 control points 11 knots Uniform, nonrational B-splines Uniform: knots are spaced at equal intervals of parameter t Nonrational: curve does not use ratios of two polynomial equations Joining: C2 continuity is guaranteed Locality: only the 4 segments containing a ctrl pt are affected Invariance: affine, not perspective Convex hull property for each segment Other issues: No control points are interpolated Overlapping control points allow interpolation But at the cost of continuity Nonuniform, nonrational B-splines Nonuniform: curve segs can be defined over non-equal intervals of t As before but: Can overlap knots

3 Allows interp points with better control of continuity Can insert knots Allows arbitrary control of locality Nonuniform, rational B-splines (NURBS) Rational: x(t) = X(t)/W(t), y(t) = Y(t)/W(t). Like before but: Invariance: affine and perspective Can define conics (circles, parabolas ) Patches A generalization from 1D to 2D Most of previous discussion applies General approach Intuitively Imagine sweeping a parametric curve Q(s) along a dimension t The geometry vector, and thus the shape of Q(s), changes as a function of t You defined an arbitrarily shaped patch! In equations P(s,t) = S M Q(t) = S M G M^t T^t Accuracy: polys/lines are always only approximations of curves Succinctness: need lots of polys to describe one smooth surface Modeling: controlling shape twiddling vertices is annoying, need higher level control Problems Rendering speed Complex models may have as many patches as polygonal models Rendering patches is slower Portability Patches are not the lowest common denominator Subdivision surfaces New research Give much better control of locality A patch can be subdivided into smaller patches In this way, a hierarchy of locality is formed Volume representations Constructive Solid Geometry (CSG) Domain: engineering and machining Shapes are described with set operations on primitive volumes Union, difference, intersection Such a description describes how to construct a shape A binary tree Leaves are primitives Internal nodes perform set ops on children A continuous description Describes the modeling process Some powerful modeling functionality Disadvantages

4 Joins are all discontinuous Poor control of locality Hard to render Discrete volume reps (voxels) Domain: largely medicine and science We surround the shape (or region) with a box We sample the entire box with a regular grid Each sample is called a voxel (volume pixel) So there are many shapes in the volume a 3D grid of points, with at least one value at each point value represents density in most settings Captures interiors well Disadvantages Takes up a lot of space! 256^3 * 4 = 67 megs basic input Fairly hard to render Ray tracing Surface extraction using Marching Cubes Alg This is changing with Mitsubishi card Implicit functions Domain: amorphous, merging shapes; bounding volumes We have a function in 3D space The surface is all points w/ same value in that func: isosurface f(x,y,z) = k Example: sphere x^2 + y^2 + z^2 = 1 We add complexity with collections of these A collection of generator shapes For each of these generators g A distance function d which returns distance from g A potential function f assigns a value to each distance d A blending function B merges potentials often simple addition Strengths Good for collision detection Can represent conics like spheres, cylinders Used in blobby modeling for molecules, etc Can merge shapes by moving the generators Problems: Constraints needed. Semicircle? Joining is a problem unwanted merges/seps Also hard to render: voxelize, ray trace New work addresses some control problems Other and newer approaches BSP trees These can also be used as modeling reps Advantage in hierarchical formation Enables set ops and fast collision detection Main problem:

5 avoiding polygonal explosion makes implementation very hard Particle systems Domain: for moving clouds of things, e.g. clouds, fireworks, flocks : A set of points or particles Each point has a largely random behavior and a lifespan Particles are rendered as blurs onscreen or sprites Fractal models Domain: clouds, mountains, sea Fractals are used to generate shape procedurally Usually these are converted into another representation L-systems Domain: plant description Grammars used procedurally to describe plant development Eventually converted into another representation Distance fields Can be viewed as adaptively sampled implicit function Because it is a sampled rep, can deviate from functional limitations Point representations Domain: very large (1 billion vertex) models Like polygons, but no edges, just vertices Vertices have associated color, orientation Basic problem: how to fill the gaps? Splatting from volume rendering Colors are blurred across local screen region Until recently, main problem was aliasing

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