Problem in Deducing Perspective Projection Matrix I understand the traditional way(use similar triangle and make depth value linear) to deduce the perspective projection matrix.
But I want to try another approach after I read this text: Fundamentals of Texture Mapping and Image Warping.
On page 17, it says that a quad can be mapped to a square using projective transformation, which can be expressed as a rational linear mapping:
$$\mathit{x} = \frac{\mathit{a}\mathit{u} + \mathit{b}\mathit{v} + \mathit{c}}
{\mathit{g}\mathit{u} + \mathit{h}\mathit{v} + \mathit{i}}\\
\mathit{y} =
\frac{\mathit{d}\mathit{u} + \mathit{e}\mathit{v} + \mathit{f}}
{\mathit{g}\mathit{u} + \mathit{h}\mathit{v} + \mathit{i}}$$
After I substitute four vertices of the quad and square, I get a linear system. By solving the linear system I can get the projective transformation matrix. 
Similarly, I conceive that a 3D projective mapping can be denoted as a rational linear mapping as well. And this rational linear mapping can map a frustum to a NDC cube.(note that because of the use of homogeneous coordinate, the last element of matrix(right bottom one) can be set to 1)
$$
\mathit{x} =
\frac{\mathit{a}\mathit{u} + \mathit{b}\mathit{v} + \mathit{c}\mathit{w} + \mathit{d}}
{\mathit{m}\mathit{u} + \mathit{n}\mathit{v} + \mathit{o}\mathit{w} + 1}\\
\mathit{y} =
\frac{\mathit{e}\mathit{u} + \mathit{f}\mathit{v} + \mathit{g}\mathit{w} + \mathit{h}}
{\mathit{m}\mathit{u} + \mathit{n}\mathit{v} + \mathit{o}\mathit{w} + 1}\\
\mathit{z} =
\frac{\mathit{i}\mathit{u} + \mathit{j}\mathit{v} + \mathit{k}\mathit{w} + \mathit{l}}
{\mathit{m}\mathit{u} + \mathit{n}\mathit{v} + \mathit{o}\mathit{w} + 1}
$$
But when I try to solve this system, the result matrix is not as same as the one in 3D API(like OpenGL) specification.
My question is: is there any extra properties of a perspective projection matrix that a rational linear mapping does not have?
EDIT: I found that in the 2D version we have 8 unknowns(3*3, and one matrix element set to 1 excluded), which equals exactly the number of equations in the linear system(4 vertices in a quad, and 2 corrdinate components in each of them). However in the 3D version, the number of unknowns and the number of equations does not match.
I suspect I misunderstand the rational linear mapping, and the reason why it can applied to a 2D version is just a coincidence.
I will do more learn on this topic.
 A: In $2D$, a perspective map can map any $4$ non-collinear points to any $4$ other points. Therefore, a perspective map has $8$ ($4\times2$) independent elements ($3\times3-1$).
In $3D$, a perspective map can map any $5$ non-coplanar points to any $5$ other points. Therefore, a perspective map has $15$ ($5\times3$) independent elements ($4\times4-1$).
In this answer, I develop the $2D$ matrix to map any $4$ non-collinear points to any $4$ other points. This method can be extended to $3D$.

A $3D$ point can be imbedded in $4D$:
$$
[x,y,z]\mapsto[x,y,z,1]
$$
and a $4D$ point can be projected to $3D$:
$$
[x,y,z,r]\mapsto\left[\frac{x}{r},\frac{y}{r},\frac{z}{r}\right]
$$
A perspective mapping imbeds a $3D$ point in $4D$, performs a $4\times4$ matrix multiplication, then projects the $4D$ result back to $3D$: 
$$
M:\left[\begin{array}{cc}x&y&z\end{array}\right]\mapsto\left[\begin{array}{ccc}x&y&z&1\end{array}\right]M=\left[\begin{array}{ccc}u&v&w&s\end{array}\right]\mapsto\left[\begin{array}{cc}\frac{u}{s}&\frac{v}{s}&\frac{w}{s}\end{array}\right]
$$
Given any $5$ points in $\mathbb{R}^3$, $[x_n,y_n,z_n]_{n=1}^5$, no $4$ of which are coplanar, compute
$$
\left[\begin{array}{ccc}d_1&d_2&d_3&d_4\end{array}\right]=\left[\begin{array}{ccc}x_5&y_5&z_5&1\end{array}\right]\left[\begin{array}{ccc}x_1&y_1&z_1&1\\x_2&y_2&z_2&1\\x_3&y_3&z_3&1\\x_4&y_4&z_4&1\end{array}\right]^{-1}
$$
and define
$$
M_{[x\;y\;z]}=\left[\begin{array}{ccc}d_1x_1&d_1y_1&d_1z_1&d_1\\d_2x_2&d_2y_2&d_2z_2&d_2\\d_3x_3&d_3y_3&d_3z_3&d_3\\d_4x_4&d_4y_4&d_4z_4&d_4\end{array}\right]
$$
Then for $5$ other points $\mathbb{R}^3$, $[u_n,v_n,w_n]_{n=1}^5$, we have
$$
M_{[x\;y\;z]}^{-1}M_{[u\;v\;w]}:\left[\begin{array}{cc}x_n&y_n&z_n\end{array}\right]\mapsto\left[\begin{array}{cc}u_n&v_n&w_n\end{array}\right]
$$
