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Thursday, September 29 (Week 6) 
 Summary of coordinate systems we have seen:
 (designer creates >) Model coordinates
 (model transformation >) World coordinates
 (view transformation >) Eye coordinates
 (projection transformation >) Clip coordinates
 (perspective division >) Normalized device coordinates (NDC)
 (viewport transformation >) Window coordinates
 Clip coordinates are 4dimensional homogeneous coordinates with a wcomponent that is not normally equal to 1 (if perspective projection is being used)
 NDC are 3dimensional with x, y, and z between 1 and 1
 z is NOT linearly related to the original zvalue in eye space (if perspective projection is being used).
 In fact $z_{NDC}$ is proportional to $\frac{1}{z_{eye}}$, so it does preserve correct depth ordering
 Window coordinates are what we see as gl_FragCoord.xy
 By default, the viewport transformation scales x and y to range from 0 to the framebuffer width/height
 The viewport transformation is set by the function
gl.viewport( $x_0$, $y_0$ width, height)
 maps NDC x value from [1, 1] into [$x_0$, $x_0$ + width] in window coordinates
 maps NDC y value from [1, 1] into [$y_0$, $y_0$ + height]
 (These are simple FahrenheittoCelsius linear rescalings)
 gl_FragCoord.z is (usually) rescaled to be from 0 to 1
 this can be configured with
gl.depthRange(near, far)
 Introducing the idea of the CS336Object from hw3
 The convention TRS: scale, then rotate, then translate
 encapsulate the scale, rotation, and translation for an object in a scene
 instead of calculating matrices, provide operations such as "move forward" or
"turn right" or "look at"

GL_example1a_resizable.html
CS336Object.js


Tuesday, September 27 (Week 6) 
 When we think about Euler angles, the most convenient convention for us is usually
the ordering YXZ, known as "headpitchroll"
 Spherical coordinates
 Using head and pitch angles to describe a direction
 Rotations about an arbitrary axis
 One strategy:
 Let $YX$ denote the pitch and head rotation needed to align the yaxis with the desired axis of rotation.
 Let $\theta$ be the desired angle of rotation
 Then $YX$ * RotateY($\theta$) * $X^{1}Y^{1}$ is the rotation matrix
 Or, use the THREE.Matrix4 method
makeRotationAxis (See #2 in the animation loop for RotatingCubeAxisTest.js )
 Issues with Euler angles
 Unnatural and confusing in many cases
 Subject to "gimbal lock"  losing a degree of freedom when two of the axes end up aligned with each other
 Difficult to interpolate between rotations for animation
 You can't just linearly interpolate between rotation matrices, since the intermediate values aren't rotation matrices!
 Example: what matrix is halfway between these two rotations, if you just average the corresponding entries?
\[\begin{bmatrix}1 & 0 \\ 0 & 1 \end{bmatrix}
\longrightarrow
\begin{bmatrix}0 & 1 \\ 1 & 0 \end{bmatrix}
\]
 Every combination of Euler angles can be described as a single rotation with some axis and angle
 For every orthogonal 3d matrix $M$ there is a vector $\vec{v}$ such that
$M\vec{v} = \vec{v}$ (an eigenvector with eigenvalue 1)
 Therefore $\vec{v}$ is the actual axis of rotation
 A quaternion describes a rotation as four numbers representing an axis and an angle
 The quaternion for angle $\theta$ with axis $\shortcvec{v_1}{v_2}{v_3}^T$ is, literally, the four numbers:
\[\cvec{\sin\left(\frac{\theta}{2}\right) v_1}
{\sin\left(\frac{\theta}{2}\right) v_2}
{\sin\left(\frac{\theta}{2}\right) v_3}
{\cos\left(\frac{\theta}{2}\right)}
\]
 The scaling by sine and cosine looks bizarre, but it's basically there to make the multiplication operations work nicely...
 ...but we don't care; we rely on library functions to do the gory stuff
 There are library functions to create a quaternion from a rotation matrix or axis/angle, and to create a rotation matrix from a quaternion
 The three.js library internally converts all rotations to quaternions
 Most importantly: Quaternions can be linearly interpolated to get smooth animation between rotations
 Spherical linear interpolation of quaternions (slerp) using the three.js
library
 Compare the ad hoc rotation about the quaternion axis (around lines 445455 of RotationsWithQuaternion.js) to the use of the slerp method (line 477 of RotationsWithQuaternionSlerp.js)

 See chapter 7 of the Gortler book for a brief, graphicscentric explanation of quaternions (available online through ISU library, see the Resources page)
 Code examples:
RotatingCubeAxisTest.html
RotationsWithQuaternion.html
(Put the model into any rotation, then press the 'a' key to see it rotate back to the identity, about the quaternion axis (the magenta line) Use 'A' and 'a' to do it again.)
See function calculateAxisAngle around line 250 to see how the three.js library is being used to get the axis and angle from the quaternion.
RotationsWithQuaternionSlerp.html


Thursday, September 22 (Week 5) 
 A bit more about perspective, depth, and the pipeline
 Homogeneous coordinates are what we feed in to the pipeline from the vertex
shader, and are used throughout the rasterization process
(see GL_example1a_homogeneous)
 After rasterization, all coordinates are divided through by their
wcomponent to obtain Normalized Device Coordinates (NDC)
 This step is called perspective division
 Normalized device coordinates are in range [1, 1]
 The x and y values are rescaled to the canvas width and height, respectively,
to get the values we see as gl_FragCoord.xy
 The z value is normally rescaled to the range [0, 1]
 A perspective projection maintains the linear relationships in the x and y
values, but the mapping from camera z to NDC z is highly nonlinear
 zfighting
 Depth buffer has finite precision, so when zvalues are close together,
the roundoff errors may cause a farther surface to "bleed" through to a closer one
 The problem is worsened by the nonlinearity of the perspective projection,
especially when the near plane is close to the camera
(See
depth_graph.pdf)
 Arts and crafts!
 Use a physical object to help visualize 3D rotations
 Any possible orientation of the coordinate axes that doesn't change the lengths or the angles between the basis vectors, is a rotation
 If you write down the coordinates of the transformed basis vectors as columns of a matrix, that's a rotation matrix
 A rotation matrix is always orthogonal (the columns are an orthonormal set of vectors comprising the basis for the rotated frame)
 But it's not necessarily a rotation about one of the coordinate axes
 Composing rotations about the three coordinate axes
 Euler angles
 Choose an ordering of two or three axes, such as YZY or XYZ
 Any rotation can be obtained as a sequence of three rotations about that sequence of axes

GL_example1a_homogeneous.html
Zfighting.html
Rotations.html


Tuesday, September 21 (Week 5) 
 Example: consider a translation $T$ and consider a set of translated points $\mathcal{F}T\underline{c}$. When we apply another transformation $R$, we can either do
 $\mathcal{F}RT\underline{c}$  $R$ is applied wrt the original frame $\mathcal{F}$ ("extrinsically")
 $(\mathcal{F}T)R\underline{c}$  $R$ is applied wrt the transformed (local) frame $\mathcal{F}T$ ("intrinsically")
 Try the first two radio buttons in Transformations2.html
 The "leftof"" rule: a transformation matrix is always applied with respect to the basis immediately to its left
 To perform a transformation $M$ wrt some other frame $\mathcal{F'} = \mathcal{F}A$, use the matrix $AMA^{1}$
 Spinning cube example
 updates the model transformation once each frame, multiplying it by a onedegree rotation about one of the axes
 Edit the animation loop (lines 255295 of RotatingCube.java) to see the difference between multiplying on the left vs multiplying on the right
 Orthographic vs perspective projections
 Homogeneous coordinates revisited
 For any nonzero $w$, $\cvec{wx}{wy}{wz}{w}^T$ describes the same 3D
point as $\cvec{x}{y}{z}{1}^T$
 This representation allows us to do a perspective projection by matrix multiplication
 Also allows the rasterizer to more efficiently do perspectivedependent calculations
 The first three coordinates are then divided by the $w$coordinate to recover a 3D point in "Normalized Device Coordinates" (NDC)
 (This operation is called perspective division)
 Deriving a basic perspective matrix using similar triangles: if the center of projection is at the camera position, and the projection plane is $n$ units in front of the camera at $z = n$, then the projected values $x$ and $y$ values are $x' = \frac{xn}{z}$ and $y' = \frac{yn}{z}$. Note:
\[\begin{bmatrix} 1 & 0 & 0 & 0 \\
0 & 1 & 0 & 0 \\
0 & 0 & 1 & 0 \\
0 & 0 & \frac{1}{n} & 0
\end{bmatrix}
\begin{bmatrix}x \\ y \\ z \\ 1 \end{bmatrix}
= \begin{bmatrix}x \\ y \\ z \\ \frac{z}{n} \end{bmatrix}
\mbox{which is the same point as}
\begin{bmatrix} \frac{xn}{z} \\ \frac{yn}{z} \\ n \\ 1 \end{bmatrix}
\]
 Basic perspective matrix above works great for $x$ and $y$, but loses depth information since all $z$values are collapsed to $n$.
 The standard OpenGL perspective matrix performs the same transformation on $x$ and $y$, but uses some mathematical trickery to keep some information about $z$ too
 The THREE.Matrix4 function
makePerspective specifies the viewing region as a rectangular frustum (chopped off rectangular pyramid) using left, right, top, bottom to describe dimensions of the near plane
 See also the helper function
createPerspectiveMatrix (in cs336util.js), which specifies the viewing region using a field of view angle plus an aspect ratio

 The section "Specifying the Visible Range (Pyramid)" in teal book ch. 7 illustrates the perspective projection
 This excellent page by Song Ho Anh goes over the derivation of the perspective matrix (and has some illustrations of the "viewing frustum")
 Experiment with perspective matrices in the rotating cube example (lines 90  110)
 Code examples:
RotatingCube.html
DepthWithPerspective.html
(You can see the basic perspective matrix created around line 66 of the .js file. Notice we have lost
the depth information, since all zvalues are 4.)


Thursday, September 15 (Week 4) 
 In almost all cases, we think of all vertices as being transformed by
three matrices, called the model, view, and projection
(i.e. projection * view * model)
 The model transformation takes model coordinates to world coordinates (i.e. places an instance of a model in the scene)
 view transformation takes world coordinates to eye (camera) coordinates
 projection transformation takes eye coordinates to clip coordinates
(determines the visible region of the scene)
 Last time: if point $\widetilde{p}$ has coordinates $\underline{c}$ w.r.t. a frame $\mathcal{F}$, and $M$ transforms $\mathcal{F}$ to a new frame $\mathcal{F}' = \mathcal{F}M$, then $\widetilde{p}$ has coordinates $M^{1}\underline{c}$ w.r.t. $\mathcal{F}'$.
 This is how we define a camera or view matrix: If $M$ is the transformation to the camera frame, then multiplying by $M^{1}$ gives you the coordinates of everything in the scene, relative to the camera.
 The idea of the projection matrix is to choose what region gets mapped into clip space. By default, this is just a cube centered at the camera's origin that goes from 1 to 1 in all three dimensions.
 Specified by six "clipping planes" defining a rectangular
region relative to the camera frame
 Deriving an "orthographic" projection matrix: ranges [left, right], [bottom, top], and [near, far] are all scaled into [1, 1] using three Fahrenheit to Celsius conversions
 An orthographic projection does not account for perspective
 See comment #8 in
DepthWithView.js, about line 144
 See this derivation by Song Ho Anh
 Digression:
 With an orthonormal basis, we can calculate the dot product of vectors
from their coordinates (like in a physics book!)
 If $\mathcal{B}$ is an orthonormal basis, $\vec{u}$ and $\vec{u}$ have coordinates $\underline{c}$ and $\underline{d}$, respectively, w.r.t. $\mathcal{B}$, then
$\vec{u} \cdot \vec{v}$ = $\underline{c}^T\underline{d}
= c_1d_1 + c_2d_2 + c_3d_3$
 When $\vec{u}$ and $\vec{v}$ are unit vectors, $\vec{u} \cdot \vec{v}$ is the cosine of the angle between them
 Thus $\vec{u} \cdot \vec{v}$ is 1 when $\vec{u}$ and $\vec{v}$ are parallel, and is 0 when they are orthogonal
 A matrix is orthonormal (sometimes called orthogonal) if its columns represent unitlength vectors that are orthogonal to each other.
 Key point: If $A$ is orthogonal, then $A^{1} = A^T$ (the inverse is just the transpose)
 The lookAt matrix: define the camera frame by specifying:
 $\widetilde{\rm{eye}}$  where is the camera?
 $\widetilde{\rm{at}}$  what's it pointed at?
 $\vec{up}$  which way is up?
 Calculate basis $\shortcvec{\vec{x}}{\vec{y}}{\vec{z}}$ for camera with two cross products:
 $\vec{z} = \widetilde{\rm{eye}}  \widetilde{\rm{at}}$, normalized
 $\vec{x} = \vec{up} \times \vec{z}$, normalized
 $\vec{y} = \vec{z} \times \vec{x}$
 $R$ = matrix whose columns are the coordinates of
$\vec{x},\vec{y},\vec{z}$. $R$ is orthogonal, so $R^{1} = R^T$
 $T$ = Translate($\widetilde{\rm{eye}})$, so $T^{1}$ = Translate($\widetilde{\rm{eye}}$)
 view matrix is inverse of $TR$, which is $R^TT^{1}$, i.e., the transpose of $R$ times Translate($\widetilde{\rm{eye}}$)
 See comment #9 in DepthWithView.js, about line 154

 Code examples: Work through the detailed comments numbered 1 through 9  uncomment the relevant code to try each one out
DepthWithView.html


Tuesday, September 13 (Week 4) 
 An affine matrix is any 4x4 matrix with $\cvec{0}{0}{0}{1}$ in bottom row
 The product of affine matrices is an affine matrix
 An affine matrix represents an affine transformation
 An affine matrix $M$ can always be decomposed into
a linear (or "rotational") part $R$ followed by a translational part $T$, that is, $M = TR$ where $T$ is a translation and $R$ is linear
\[
\begin{bmatrix}1 & 0 & 0 & t_x \\
0 & 1 & 0 & t_y \\
0 & 0 & 1 & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}a & d & g & 0 \\
b & e & h & 0 \\
c & f & i & 0 \\
0 & 0 & 0 & 1
\end{bmatrix}
=
\begin{bmatrix}a & d & g & t_x \\
b & e & h & t_y \\
c & f & i & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\]
 Note that $RT$ is also an affine transformation, and its linear part is $R$, but its translational part is not $T$. That is, $RT = T'R$, where $T'$ is a translation that is generally not equal to $T$.
 Matrix inverse: $AA^{1} = A^{1}A = I$
 Why $(AB)^{1} = B^{1}A^{1}$
 Not every matrix has an inverse
 Standard transformation matrices are easy to invert, e.g.
inverse of Scale(2, 3, 1) is Scale(1/2, 1/3, 1)
 Observation: Suppose point $\widetilde{p}$ has coordinates \underline{c} with respect to frame $\mathcal{F}$, and we transform by matrix $M$ to get a point
$\widetilde{p} = \mathcal{F}(M\underline{c})$.
There are two ways to look at this new point:
 $\mathcal{F}(M\underline{c})$  new coordinates, interpreted w.r.t. existing frame $\mathcal{F}$
 ($\mathcal{F}M)\underline{c}$  same coordinates, interpreted w.r.t a transformed frame $\mathcal{F}M$
 For an affine matrix $M$ and frame $\mathcal{F}$, the product $\mathcal{F}M$ is also a frame, transformed by $M$
 A different question: suppose we want to keep the same point, but find its new coordinates w.r.t a transformed frame $\mathcal{F}M$?
 Example: if $\underline{c}$ is the coordinate vector that takes you from Main and Duff to Steve's house, and $M$ is the transformation that takes Main and Duff to the Clocktower, then how do you get from the Clocktower to Steve's house?
 First invert $M$ to get from Clocktower to Main & Duff
 Then use coordinates $\underline{c}$ to get to Steve's.
 That is, $M^{1}\underline{c}$ gives you the coordinates of Steve's house with respect to the Clocktower
 More generally, if point $\widetilde{p}$ has coordinates $\underline{c}$ w.r.t. a frame $\mathcal{F}$, and $\mathcal{F}' = \mathcal{F}M$, then $\widetilde{p}$ has coordinates $M^{1}\underline{c}$ w.r.t. $\mathcal{F}'$. That is:
$$\begin{align}
\mathcal{F}' &= \mathcal{F}M \\
\Longrightarrow \mathcal{F}'M^{1} &= \mathcal{F} \\
\Longrightarrow \mathcal{F}'M^{1}\underline{c} &= \mathcal{F}\underline{c} = \widetilde{p}
\end{align}$$
 The depth buffer and gl_FragCoord.z
 The depth buffer algorithm for hidden surface removal
initialize all locations of depth buffer to "infinity"
in each fragment (x, y)
let z' = value in depth buffer at (x, y)
if gl_FragCoord.z < z'
set depth buffer value at (x, y) to gl_FragCoord.z
run the fragment shader
else
do nothing

Clip space is lefthanded!
 larger zcoordinate means "farther away"

 The section "Correctly Handling Foreground and Background Objects" in teal book ch. 7 describes the depth buffer
 The idea of finding the coordinates of a point with respect to some other frame has a lovely explanation in Chapter 13 ("Change of basis") of the linear algebra videos at 3blue1brown.com. Chapters 2, 3, and 4 are also relevant to what we have been doing lately.
 Code examples: Experiment with compositions of transformations using the key controls:
Transformations2.html


Thursday, September 8 (Week 3) 
 Composing transformations is matrix multiplication
 If $R$ and $S$ are matrices for two transformations and $\underline{c}$ is a coordinate vector, then
\[
(SR)\underline{c} = S(R\underline{c})
\]
that is, the matrix $SR$ represents the transformation that does $R$ first, then $S$
 In general $SR \neq RS$, for example, "rotate and then scale" is almost never the same transformation as "scale and then rotate"
 Overview of matrix operations from the three.js library
 Recall that we can use 4d homogeneous coordinates to represent 3d points and vectors.
 The 4th coordinate is 0 for a vector and 1 for a point
 A linear transformation can be represented by a 4x4 matrix whose bottom row and right column are both $\cvec{0}{0}{0}{1}$ (The upper left 3x3 submatrix is the same as what we derived last time.)
 An affine transformation is a linear transformation followed by a translation (shift)
 in one dimension: $f(x) = mx + b$
 A translation is represented by a matrix of the form below:
\[
\begin{bmatrix}1 & 0 & 0 & t_x \\
0 & 1 & 0 & t_y \\
0 & 0 & 1 & t_z \\
0 & 0 & 0 & 1
\end{bmatrix}
\begin{bmatrix}x \\ y \\ z \\ 1 \end{bmatrix}
=
\begin{bmatrix}x + t_x \\ y + t_y \\ z + t_z \\ 1 \end{bmatrix}
\]
 A vector with length 1 is called a unit vector
 A nonzero vector can always be divided by its length to create a unit vector with the same direction (called "normalizing" the vector)
 The dot product between two unit vectors is the cosine of the angle between them
 Two nonzero vectors are orthogonal (perpendicular) if their dot product is zero
 An orthonormal basis consists of three unit vectors that are orthogonal to each other
 A standard basis is an orthonormal basis that is righthanded
 For any standard basis, a general rotation matrix (for rotations of angle $\theta$ about the zaxis) looks like this:
\[
\begin{bmatrix}\cos(\theta) & \sin(\theta) & 0 & 0 \\
\sin(\theta) & \cos(\theta) & 0 & 0 \\
0 & 0 & 1 & 0 \\
0 & 0 & 0 & 1
\end{bmatrix}
\]
 "Pseudocode" for standard matrices
 RotateX($\theta$), RotateY($\theta$), RotateZ($\theta$)
 Scale($s_x, s_y, s_z$)
 Translate($t_x, t_y, t_z$)

 See sections 3.2  3.5 of Gortler (ignore 3.6 on normal vectors for now)
 The teal book section "Translate and Then Rotate" in ch. 4 goes into composing multiple transformations
 You'll need the Matrix4 type from three.js. You can download the core library from Canvas, see link #6 on our Canvas front page. See
threejs.org
for more info.
 See the documentation for the three.js Matrix4 type
 Also see chapter 4 of the Linear Algebra series by 3blue1brown:
https://www.3blue1brown.com/topics/linearalgebra
 Code examples (edit the main method to try different transformations):
Transformations1.html


Tuesday, September 6 (Week 3) 
 Using an index buffer to select order of vertices
 Linear transformations
 key feature: if you transform points that are collinear, then the resulting points are also collinear...
 ...so triangles are transformed to triangles
 For a given basis
$\mathcal{B} = \shortcvec{\vec{e_1}}{\vec{e_1}}{\vec{e_1}}$, a linear transformation $f$ is represented by a matrix $M$ such that if $\underline{c}$
is a coordinate vector w.r.t. $\mathcal{B}$ for some vector $\vec{u}$, then
$M\underline{c}$ is the coordinate vector for the transformed vector $f(\vec{u})$
 The columns of $M$ are just the coordinate vectors for
$f(\vec{e_1}), f(\vec{e_2})$, and $f(\vec{e_3})$
 Examples of 3d linear transformations and their matrices  90 degree rotation, scaling
 Using
gl.uniform4fv to pass matrix data to the GPU
 Matrix data is passed to the GPU in columnmajor order

 See sections 2.3 and 3.3 of Gortler
 In the teal book, the section "Moving, Rotating, and Scaling" in chapter 3 is a basic introduction to transformations
 Also see chapter 3 of the Linear Algebra series by 3blue1brown:
https://www.3blue1brown.com/topics/linearalgebra
 Code examples (edit the main method to try different transformations):
Transformations.html


Thursday, September 1 (Week 2) 
 In graphics, we need to deal with multiple "frames of reference" (coordinate systems)
 Preview of typical vertex processing steps:
 Model transformation places instance of model into world coordinates
 View transformation transforms world into eye/camera coordinates (scene from eye/camera view point)
 Projection transformation transforms a viewable region of scene into clip coordinates (a 2x2x2 cube representing the "viewable" triangles to be handed to the rasterizer)

 A point is a geometric location (and may
have different coordinates depending on the frame)
 The difference between two points is a vector, representing the distance and direction from one point to another
 A point plus a vector is a point
 Vector operations  scaling and addition
 Linear combinations of vectors (scale and add)
 A 3d vector space consists of all 3D vectors with the basic operations above
 A 3d basis for a vector space is a set of three independent vectors
$\vec{b_1}, \vec{b_2}, \vec{b_3}$
such that every possible 3d vector can be written as a linear combination of $\vec{b_1}, \vec{b_2}, \vec{b_3}$
 A 3d affine space consists of all 3d vectors and points
 A 3d frame (coordinate system) is a basis along with a designated point called the origin
 Matrices and matrix multiplication
 Representing a basis as a row matrix of three vectors,
$\mathcal{B} = \shortcvec{\vec{b_1}}{\vec{b_2}}{\vec{b_3}}$
 Representing a frame as a row matrix of three vectors and a point,
$\mathcal{F} = \cvec{\vec{b_1}}{\vec{b_2}}{\vec{b_3}}{\widetilde{o}}$
 If vector $\vec{u} = u_1\vec{b_1} + u_2\vec{b_2} + u_3\vec{b_3}$, then the column matrix \[ \underline{c} = \begin{bmatrix} u_1 \\ u_2 \\ u_3 \\ 0 \end{bmatrix}
= \cvec{u_1}{u_2}{u_3}{0}^T
\]
is a coordinate vector for $\vec{u}$ with respect to frame $\mathcal{F}$. Note that in terms of matrix multiplication, we can write
\[
\mathcal{F}\underline{c} = \cvec{\vec{b_1}}{\vec{b_2}}{\vec{b_3}}{\widetilde{o}}\cvec{u_1}{u_2}{u_3}{0}^T = \vec{u}
\]
(where the superscript $T$ indicates the matrix transpose)
 Likewise, if point $\widetilde{p} = u_1\vec{b_1} + u_2\vec{b_2} + u_3\vec{b_3} + \widetilde{o}$, then the column matrix $\cvec{u_1}{u_2}{u_3}{1}^T$ is a coordinate
vector for $\widetilde{p}$ w.r.t. frame $\mathcal{F}$,
 Key point: the coordinates of a vector or point depend on what basis or frame is being used, and the coordinates only have meaning with respect to a basis or frame

 Read Sections 2.1, 2.2, and 3.1 of the book by Gortler (Foundations of 3D computer graphics). This is just a few pages and summarizes pretty much everything we talked about today. Just log into the ISU library and search for the title. The entire book is available online. You will need to log into the ISU VPN if you are off campus.
 Review basic matrix operations if you have not seen them before
 For nice visuals and simple explanations of vectors and linear combinations, see chapters 1 and 2 (the second and third videos) of the Linear Algebra series by 3blue1brown:
https://www.3blue1brown.com/topics/linearalgebra


Tuesday, August 30 (Week 2) 
 Recap of basic steps in our Hello, World example
 The purpose of an
attribute variable in the vertex shader is to pass pervertex data from CPU to GPU
 The function
vertexAttribPointer associates the vertex attribute
with the data in your buffer
 Vertex shader must always set the builtin variable
gl_Position
 Fragment shader normally sets the builtin variable
gl_FragColor
 The builtin variable
gl_FragCoord available in the fragment shader
gl_FragCoord.x and gl_FragCoord.y represent the position of the fragment within the framebuffer, ranging from 0 up to the canvas width/height
 (
gl_FragCoord.z is the "depth" information, stored in a separate buffer called the depth buffer. It ranges from 0 to 1.)
 Animation by updating a uniform variable in each frame
 Using
requestAnimationFrame to create animation loop
 There are three kinds of variable modifiers in GLSL
 attribute variables are used to pass pervertex data from the CPU to GPU
 uniform variables are used to pass uniform data from CPU to GPU
(same value in every vertex/fragment shader instance)
 varying variables are used to pass data from vertex shader to fragment shader (values are interpolated by the rasterizer)
 Using functions such as
uniform1f  set a uniform with one float value
uniform4f  set a vec4 uniform with four floating point values
uniform4fv   set a vec4 uniform with an array of values
 etc...
 See the "WebGL Reference Card" for GLSL types and functions
 Linear interpolation!
 Basic example: Converting Fahrenheit to Celsius
 Suppose we have some Farenheit temperature $x$ and we want the Celsius temperature $y$. Then
$$\beta = \frac{x  32}{212  32}$$
tells you "how far" $x$ is along the scale from 32 to 212. We want to go the same fraction of the way along the Celsius scale from 0 to 100, i.e.,
$$y = 0 + \beta(100  0)$$
To say that the Fahrenheit and Celsius scales are "linearly related" just means, e.g., that if we are 25% of the way from 32 to 212 in Fahrenheit, we should be 25% of the way from 0 to 100 in Celsius.
More generally, to map the 32to212 scale to any range $A$ to $B$, you have $$\begin{eqnarray}
y &=& A + \beta\cdot(B  A) \\
&=& (1  \beta)\cdot A + \beta\cdot B \\
&=& \alpha\cdot A + \beta\cdot B
\end{eqnarray}$$
where $\alpha = 1  \beta$.
One way to think of this is that you have two quantities $A$ and $B$ to be "mixed", and the proportion $\beta$ tells you "how much $B$" and $\alpha$ is "how much $A$".
This is nice because it generalizes to interpolating within a triangle using barycentric coordinates.

 Code examples:
 Note: GL_example1a is the same as GL_example1 except that all the
boilerplate helper functions have been moved into ../util/cs336util.js
GL_example1a.html
GL_example1a_gradient.html
GL_example1a_with_animation.html
GL_example2_varying_variables.html


Thursday, August 25 (Week 1) 
 Odds and ends:
 Basic application structure; the
onload event in JS. See
foo.html and foo.js
(output goes to JS console).
 Setting up a WebGL context and clearing the canvas (see GL_example0)
 RGBA encoding of color as four floats
 The graphics context is a state machine (function calls rely on lots of internal state)
 The idea of binding a buffer or shader to become "the one I'm currently talking about"
 Binding points ARRAY_BUFFER, ELEMENT_ARRAY_BUFFER
 Overview of the steps involved in our "Hello, World!" application
 (Initialization)
 Create context
 Load and compile shaders
 Create buffers
 Bind each buffer and fill with data
 (Each frame)
 Bind shader
 For each attribute...
 Find attribute index
 Enable the attribute
 Bind a buffer with the attribute data
 Set attribute pointer to the buffer
 Set uniform variables, if any
 Draw, specifying primitive type
 Options for primitives: TRIANGLES, LINES, LINE_STRIP, etc.
 See ListExample.html for a demo

 Chapters 2 and 3 of the teal book provide a detailed and careful overview of the steps described above.
 Also highly recommended: the first chapter of
https://webglfundamentals.org/
 Read and experiment with GL_Example1 below
 try changing the vertices
 try the commentedout lines in the draw() function
 Code examples:
GL_example0.html
GL_example0.html
GL_example1.html
ListExample.html
(You can view the associated html and javascript source in the developer tools (CtrlAlti), or just grab everything directly from the examples/intro/ directory of https://stevekautz.com/cs336f22/.
)


Tuesday, August 23 (Week 1) 
 Introduction
 This is a course in 3D rendering using OpenGL, not a course in developing GUIs!
 WebGL is a set of browserbased JavaScript bindings for OpenGL ES 2.0, which is essentially OpenGL 3.2 with the deprecated stuff and fancy features removed
 Overview of the GPU pipeline:
 (Model  a set of vertices organized into a "mesh" of triangles)
 > Vertex processing (*)
 > Primitive assembly (and clipping)
 > Rasterization
 > Fragment processing (*)
 > (Framebuffer  graphics memory mapped to an actual display window)
 (*) Vertex and fragment processing stages are programmed via "shaders" using GLSL, the OpenGL shading language

 Read the syllabus
 See the Resources page for textbook information
 Learn JavaScript (see Resources page for ideas)
