# TP3 : B-splines, De Boor's algorithm

## Code

## B-splines cheatsheet

- degree
`k`

`n + 1`

control points`m + 1`

knots`m = n + k + 1`

## B-splines

Downside of Bézier splines is their global nature: moving a single control point changes the whole spline.
A possible solution to this issue are the **B-splines**.

Given a degree $k$, control polygon $\mathbf d_0,\dots,\mathbf d_n$, and a knot vector $t_0 \leq t_1 \leq \dots \leq t_m$ with $m = n+k+1$, a B-spline curve $S(t)$ is defined as

The $N_j^k$ are the recursively-defined *basis functions* (hence the name B-spline)

Looks complicated? Don’t worry if you cannot get your head around all the indices and whatnot; De Boor is here to help you!

B-spline basis functions $N^k_j$ up to degree 5 for the knot sequence $(0,1,2,3,4,5,6,7)$.

## De Boor’s algorithm

… also called the De Boor-Cox algorithm can be seen as a generalization of the De Casteljau’s algorithm. (Bézier curve is in fact a B-spline with a special knot sequence.)

- input :
$\mathbf{d_{0}},\dots,\mathbf{d_{n}}$ : control points

$t_0 \leq t_1 \leq \dots \leq t_m$ : knot vector

$t \in [t_i, t_{i+1}) \subset [t_k, t_{m-k})$ where $k = m-n-1$ is the degree - output : point $\mathbf S(t) = \mathbf d_i^k$ on the curve
- algorithm : For $j=i-k, \dots, i,$ set $\mathbf d_j^0 = \mathbf d_j$. Then compute the points \begin{align} \mathbf d_{j}^{r} &= (1-w_{j,k-(r-1)}) \mathbf d_{j-1}^{r-1} + w_{j,k-(r-1)} \mathbf d_{j }^{r-1} \end{align} for \begin{align} \quad r = 1,\dots,k, \quad \quad j = i-k+r,\dots,i \end{align} with \begin{align} w_{j,k-(r-1)} &= \frac{ t - t_j }{ t_{j+k-(r-1)} - t_j }. \end{align}

Be careful with the indices! Here we have expressed a point at depth $r$ in terms of points at depth $r-1$ – that is why there is the $r-1$ everywhere in the formula.

This might be a bit annoying, but I think it’s also more practical for the recursive implementation. (The formula becomes much more elegant if we express level $r+1$ in terms of level $r$.)

A cubic B-spline with 16 segments and endpoint interpolation.

## ToDo$^1$

- Implement the De Boor’s algorithm.
- Evaluate B-spline for the
`simple`

dataset. Modify the knot vector and recompute. What changed? - Evaluate B-spline for the
`spiral`

dataset. Modify the knot vector to`0 0 0 0 1 1 1 1 2 2 2 2 3 3 3 3 4 4 4 4 5 5 5 5`

. What changed? - Evaluate B-spline for the
`camel`

dataset. Move the front leg by changing the x-coordinate of the last control point to`-1.5`

. Which segments of the curve have changed? Why?

## NURBS

If you’ve tested with the `circle.bspline`

dataset, you were probably dissapointed:
the resulting curve is far from being a circle.

Here’s the bad news: it’s mathematically *impossible* to represent circle as a B-spline.
But, it *is possible* via a generalization called Non-Uniform Rational B-Splines or **NURBS**.

Why the long name?
*Non-uniform* simply means the knot sequence is non-uniformly spaced.
*Rational* means we work in homogeneous coordinates
by assigning a weight to each point.

In plane, the point $(x,y)$ becomes $(x,y,1) \approx (wx,wy,w)$.

If you examine `circle9.nurbs`

file, you’ll it’s quite similar to `circle.bspline`

.
The only thing that’s changed is the addition of a third coordinate – this is the weight `w`

.

And here’s the good news: even with the homogeneous coordinates, we can apply exactly the same De Boor’s algorithm **without any modifications**!

Here’s a secret recipe for transforming your B-spline code to work with NURBS:

- The columns of
`ControlPts`

read from a`.nurbs`

correspond to`x`

,`y`

and`w`

. Therefore, you first need to multiply both`x`

and`y`

(columns 0 and 1) by`w`

(column 2). - Feed the homogeneous control points
`[w*x,w*y,w]`

to the De Boor’s algorithm you’ve implemented previously. - Convert the computed points (stored in the matrix
`Segment`

) back to Cartesian coordinates. Divide by the third column to pass from`[w*x,w*y,w]`

to`[x,y,1]`

. - As before, plot the first two coordinates.

**Hint**: in Python, the operators `*`

and `/`

are applied element-wise, so you can do stuff like

```
matrix[:,0] *= matrix[:,2]
matrix[:,0] /= matrix[:,2]
```

## ToDo$^2$

- Modify your code to work in homogeneous coordinates (if
`dim=3`

). - Evaluate
`circle9.nurbs`

and`circle7.nurbs`

. Compare the results with`circle.bspline`

.

## Resources

- B-spline and De Boor’s algorithm
- 1.4.2 B-spline curve
and
1.4.3 Algorithms for B-spline curves,
online chapters from the book
*Shape Interrogation for Computer Aided Design and Manufacturing*by N. Patrikalakis, T. Maekawa & W. Cho - NURBS on wikipedia (includes the circle example)
- homepage of Prof. de Boor