\section{B-spline Surfaces}

This section is optional reading for those who want to
become acquainted with some of the mathematics of
tensor-product B-splines surfaces. For a description of the data structure for
B-spline surfaces in SISL, see the reference manual.

A tensor product B-spline surface is defined as
\[{\bf s}(u,v) = \sum_{i=1}^{n_1}\sum_{j=1}^{n_2}{\bf p}_{i,j} 
	B_{i,k_1,{\bf u}}(u) B_{j,k_2,{\bf v}}(v) \]
with control points ${\bf p}_{i,j}$ and two variables
(or parameters) $u$ and $v$.
The formula shows that a basis function of a B-spline surface is a
product of two basis functions of B-spline curves (B-splines).
This is why a B-spline surface is called a tensor-product surface.
The following is a list of the components of the representation:
\begin{description}
\item[$dim$]: The dimension of the underlying Euclidean space.
\item[$n_1$]: The number of vertices with respect to the first parameter.
\item[$n_1$]: The number of vertices with respect to the second parameter.
\item[$k_1$]: The order of the B-splines in the first parameter.
\item[$k_2$]: The order of the B-splines in the second parameter.
\item[${\bf u}$]: The knot vector of the B-splines with respect to
                  the first parameter,
                  ${\bf u} = (u_1,u_2,\ldots,u_{n_1+k_1})$.
\item[${\bf v}$]: The knot vector of the B-splines with respect to
                  the second parameter,
                  ${\bf v} = (v_1,v_2,\ldots,v_{n_2+k_2})$.
\item[${\bf p}$]: The control points of the B-spline surface,
           $c_{d,i,j}$, $d=1,\ldots,dim$, $i=1,\ldots,n_1$,
		$j=1,\ldots,n_2$.
	When $dim = 3$, we have
          ${\bf p} = (x_{1,1},y_{1,1},z_{1,1},x_{2,1},y_{2,1},z_{2,1},\ldots$,
                  $x_{n_1,1},y_{n_1,1},z_{n_1,1},\ldots$,
                     $x_{n_1,n_2},y_{n_1,n_2},z_{n_1,n_2})$.
\end{description}

The data of the B-spline surface must fulfill the following requirements:
\begin{itemize}
\item
Both knot vectors must be non-decreasing.
\item
The number of vertices must be greater than or equal to the order
with respect to both parameters: $n_1 \ge k_1$ and $n_2 \ge k_2$.
\item
There should be $k_1$ equal knots at the beginning and end 
of knot vector ${\bf u}$ and $k_2$ equal knots at the beginning and
end of knot vector ${\bf v}$.
\end{itemize}

The properties of the representation of a B-spline surface are
similar to the properties of the representation of a B-spline curve.
The control points ${\bf p}_{i,j}$ form a {\it control net} as shown in
figure~\ref{surf1}.
The control net has similar properties to the control
polygon of a B-spline curve, described in section~\ref{contrlpoly}.
A B-spline surface has two knot vectors, one
for each parameter. In figure~\ref{surf1} we can
see {\it isocurves}, surface curves
defined by fixing the value of one of the parameters.

\begin{figure}
\vspace{50 mm}
  \special{psfile=surf1.ps hoffset=-30 voffset=-170 hscale=90 vscale=60}
  %\special{psfile=surf1.ps hoffset=95 hscale=40 vscale=40}
%  \epsffile{surf1.ps}
  \caption{\label{surf1}
		A B-spline surface and its control net.
		The surface is drawn using isocurves.
		The dimension is 3.}
\end{figure}

\subsection{The Basis Functions}

A basis function of a B-spline surface is the product of two
basis functions of two B-spline curves,
\[ B_{i,k_1,{\bf u}}(u) B_{j,k_2,{\bf v}}(v). \]
Its support is the rectangle $[u_i,u_{i+k_1}] \times [v_j,v_{j+k_2}]$.
If the basis functions in both directions are of degree one and all
knots have multiplicity one, then the surface basis functions are
pyramid-shaped 
(see figure~\ref{surf2}).
For higher degrees, the surface basis functions are bell shaped.

\begin{figure}
	\begin{center}
		\begin{picture}(250,125)(0,0)

		\put(10,39){\vector(0,1){86}}
		\put(8,45){\line(1,0){4}}
		\put(8,120){\line(1,0){4}}
		\put(0,45){\makebox(0,0){0.0}}
		\put(0,120){\makebox(0,0){1.0}}

		\put(50,5){\circle*{2}}
		\put(100,5){\circle*{2}}
		\put(150,5){\circle*{2}}

		\put(190,20){\circle*{2}}
		\put(215,45){\circle*{2}}
		\put(240,70){\circle*{2}}

		\thicklines
		\put(65,20){\line(1,0){100}}
		\put(165,20){\line(1,1){50}}
		\multiput(65,20)(18,18){3}{\line(1,1){10}}
		\multiput(115,70)(20,0){5}{\line(1,0){10}}


		\put(65,20){\line(3,4){75}}
		\put(165,20){\line(-1,4){25}}
		\put(215,70){\line(-3,2){75}}
		\multiput(115,70)(8,16){3}{\line(1,2){5}}
		\end{picture}\\
	\end{center}

  \caption{\label{surf2}
		A basis function of degree one in
		both variables.}
\end{figure}

\subsection{NURBS Surfaces}

A NURBS (Non-Uniform Rational B-Spline) surface is a generalization
of a B-spline surface,
$$ {\bf s}(u,v) = {\sum_{i=1}^{n_1}\sum_{j=1}^{n_2} w_{i,j} {\bf p}_{i,j} 
	    B_{i,k_1,{\bf u}}(u) B_{j,k_2,{\bf v}}(v)  \over
             \sum_{i=1}^{n_1}\sum_{j=1}^{n_2} w_{i,j}
	         B_{i,k_1,{\bf u}}(u) B_{j,k_2,{\bf v}}(v)}. $$
In addition to the data of a B-spline surface, the NURBS surface
has a weights $w_{i,j}$.
NURBS surfaces can be used to exactly represent several common
`analytic' surfaces such as spheres, cylinders, tori, and cones.
A disadvantage is that NURBS surfaces depend nonlinearly on their weights,
making some calculations, like with NURBS curves,
less efficient.

The representation of a NURBS surface is the same as for a B-spline
except that it also includes
\begin{description}
\item[${\bf w}$]: The weights of the NURBS surface,
           $w_{i,j}$, $i=1,\ldots,n_1$, $j=1,\ldots,n_2$, so
          ${\bf w} = (w_{1,1},w_{2,1},\ldots,w_{n_1,1},\ldots$,
                  $w_{1,2},\ldots,w_{n_1,n_2})$.
\end{description}
In SISL we make the assumption that
\begin{itemize}
\item The weights are (strictly) positive: $w_{i,j} > 0$.
\end{itemize}

