I'm doing Spline interpolation in C++. I've used code from here: http://tehc0dez.blogspot.ch/2010/04/nice-curves-catmullrom-spline-in-c.html (the code is also linked on that page, it's up on github). The app is working just fine for closed contours, since it's copying the first three points to the end.
But in my case I need to be able to make an "open" shape - or rather line-, where the first and last points are not connected.
It is my understanding that since the Catmull-Rom spline is cubic, I won't be able to calculate the interpolated points for the first and last segment without adding any additional points.
I read that a common method to interpolate the points in those two segments is to use Quadratic Interpolation.
Unfortunately I can't wrap my head around how to do this. I've found out how to do quadratic Bezier approximation, but this is not what I want to do since I don't want to introduce any additional support points.
I found this site: http://dafeda.wordpress.com/2010/09/01/newtons-divided-difference-polynomial-quadratic-interpolation/ Which explains quite nicely how to do Quadratic interpolation. But I don't know how to adapt this for my case, where I want to calculate a new Point rather than just y.
Any help would be appreciated. Thanks !
The usual way to do this is to add a second copy of your two end points... So if you have a spline passing through A-B-C-D then you will calculate the spline A-A-B-C-D-D.
Managed to implement a decent solution thanks to the formula found here: http://www.doc.ic.ac.uk/~dfg/AndysSplineTutorial/Parametrics.html
They also provide a nice Java applet to check out the different parameters.
For my problem I set the t1 value to 0.5 and check if t is above/below this threshold, since I only want to draw one segment of the curve! Works out nicely.
Related
I read through the forum and as I am sure this question has been asked before, but I couldn't really find what I was looking for.
My problem is the following:
I have an AI-Character moving along a spline. Should that path be blocked, the character should move in an arc around it and then continue on it's path.
For arguments sake lets assume that the spline has a length of 7000 units.
Therefore, I have two 3D (x,y,z) vectors. The first vector is the current position of the AI-bot and the second vector the position past the obstacle. For the time being lets just say: current spline position + 400 units; later on I could do a line trace to get the dimension of the obstacle etc. but for now I don't care about it.
Now I would like to compute an alternative path to avoid aforementioned obstacle - hence compute the arc between these two points - How do I do this?
I am really terrible at maths but looked at projectile trajectory because I thought that it would be sort of the same, just was unable to really understand it :<
It doesn't have to be an arc. You can solve this problem recursively in a very simple way.
Consider you're at position A, and the obstacle is at position B. You can do the following moves:
From current position to A+V(B[x]+height(B),0,0)
From current position to A+V(0,B[y]+width(B),0)
From current position to A+V(B[x]-height(B),0,0)
where V is a vector with components V(x,y,z), width(B) is the width of the obstacle and B[x] is the x component of the position of B. This way you moved around it along a rectangle. You can now smoothen the path by subdividing that rectangle in halves. 3 subdivisions are enough to make this smooth enough. To subdivide, take the middle point the first path, and draw a line to the middle of the second path. The same you do from the second path to the third one, and now your rectangle becomes an octagon. If that's not smooth enough, do a few more steps. This will create a new spline that you can use.
I would look at a combination of splines and the EQS system. The spline defines the ideal path to follow. The EQS system finds locations near or on the path, while still doing obstacle avoidance. EQS can return all valid destinations so you can manually order them by custom critera.
Actors set on a spline do work, but there's a whole bunch o' mess when making them stop following a spline, creating a new one at the correct point, attaching the actor the new spline, and so on.
I arrived at this conclusion yesterday after exactly going the messy way of adding spline points etc. The only problem i see is that I find the EQS system very difficult to understand. Not following the examples as such, but modifying it in the way I need it. Lets see, i keep you posted.
I am currently working on a soft body system using numeric spring physics and I have finally got that working. My issue is that everything is currently in straight lines.
I am aiming to replicate something similar to the game "The floor is Jelly" and everything work except the smooth corners and deformation which currently are straight and angular.
I have tried using Cubic Bezier equations but that just means every 3 nodes I have a new curve. Is there an equation for Bezier splines that take in n number of control points that will work with loop of vec2's (so node[0] is the first and last control point).
Sorry I don't any code to show for this but i'm completely stumped and googling is bringing up nothing.
Simply google "B-spline library" will give you many references. Having said this, B-spline is not your only choice. You can use cubic Hermite spline (which is defined by a series of points and derivatives) (see link for details) as well.
On the other hand, you can also continue using straight lines in your system and create a curve interpolating the straight line vertices just for display purpose. To create an interpolating curve thru a series of data points, Catmull-Rom spline is a good choice for easy implementation. This approach is likely to have a better performance than really using a B-spline curve in your system.
I would use B-splines for this problem since they can represent smooth curves with minimal number of control points. In addition finding the approximate smooth surface for a given data set is a simple linear algebra problem.
I have written a simple B-spline C++ library (includes Bezier curves as well) that I am using for scientific computations, here:
https://github.com/feevos/bsplines
it can accept arbitrary number of control points / multiplicities and give you back a basis. However, creating the B-spline curve that fits your data is something you have to do.
A great implementation of B-splines (but no Bezier curves) exists also in GNU GSL (
https://www.gnu.org/software/gsl/manual/html_node/Basis-Splines.html). Again here you have to implement the control points to be 2/3D for the given basis, and fix the boundary conditions to fit your data.
More information on open/closed curves and B-splines here:
https://www.cs.mtu.edu/~shene/COURSES/cs3621/NOTES/index.html
I have N points in 3-dimensional space. I need to join them using a line. However, if I do that using a simple line, it is not smooth and looks ugly.
My current approach is to use a Bezier curve, using the DeCasteljau algorithm for 4 points, and running that for each group of 4 points in my data set. However, the problem with this is that since I run it on say points 1-4, 5-8, 9-12, etc., separately, the line is not smooth between 4-5, 8-9, etc.
I also looked for other approaches; specifically I found this article about Catmull-Rom splines, which seem even better suited for my purpose, because the curve passes through all control points, unlike the Bezier curve. So I almost started implementing that, but then, I saw on that site that the formula works "assuming uniform spacing of control points". That is not the case for my problem.
So, my question is, what approach should I use -- Bezier, Catmull-Rom, or something completely different? If Bezier, then how to fix the non-smoothness between 4-5, 8-9, etc.? If Catmull-Rom, why won't the formula work if points are not evenly spaced, and what do I need instead?
EDIT: I am now pretty sure I want the Catmull-Rom spline, as it passes every control point which is an advantage for my application. Therefore, the main question I would like answered is why won't the formula on the link I provided work for non-uniformly spaced control points?
Thanks.
A couple of solutions:
Use a B-spline. This is a generalization of Bezier curves (a Bezier curve is a B-spline with no internal knot points.)
Use a cubic spline. Cubic splines are particularly easy to calculate. A cubic spline is continuous in the zero, first, and second derivatives across the control points. The third derivative, the cubic term, suffers a discontinuity at the control points, but it is very hard to see those discontinuities.
One key difference between a B-spline and a cubic spline is that the cubic spline will pass through all of the control points, while a B-spline does not. One way to think about it: Those internal control points are just suggestions for a B-spline but are mandatory for a cubic spline.
A meaningful line (although not the simplest to evaluate) can be found via Gaussian Processes. You set (or infer) the lengthscale over which you wish the line to vary (i.e. the smoothness of the line) and then the GP line is the most probable line through the data given the lengthscale. You can add noise to the model if you don't mind the line not passing through the data points.
Its a nice interpolation method because you can also obtain the standard deviation of your line. The line becomes more uncertain when you don't have much data in the vacinity.
You can read about them in chapter 45 of David MacKay's Information Theory, Inference, and Learning Algorithms - which you can download from the author's website here.
one solution is the following page in wikipedia: http://en.wikipedia.org/wiki/Bézier_curve, check the generalized approach for N control points.
Suppose we have an ellipse x^2/a^2 + y^2/b^2 .
Taking a point (a*cos(t),b*sint(t)) on the ellipse, what is the fastest way to find another point on the ellipse such that distance between them is a given d. [d is less than pi*a*b].
The problem was encountered when i have a corner [quarter ellipse] and need to find points along it seperated by some 'd'.
The length of a subsection of an ellipse is an elliptic integral, with no closed form solution.
In order to compute the distance along the ellipse, you will need a numerical integration routine. I recommend Romberg, or Gauss Quadrature (look up on Wikipedia). If you are doing this repeatedly, then precompute the distance across a bunch of points around the Ellipse so that you can rapidly get to the right region, then start integrating.
You will need to bisect (look up on Wikipedia) to find the desired length.
There is no analytical solution for the length of an elliptical arc. This means you won't be able to plug numbers into an equation to find a result, but instead use a method of numerical integration.
Simpsons rule is very easy to implement although most likely slower than the methods mentioned in other answers.
Now that you have a way to find the length of an elliptical arc, just measure different end points until you find one of length d to some acceptable tolerance
As the user drags their stylus across the tablet, you receive a series of coordinates. You want to approximate the pen's path with a smooth line, trailing only a few sample points behind it. How would you do this?
In other words, how would you render a nice smooth responsive line as a user draws it with their tablet? Simply connecting the dots with straight lines is not good enough. Real drawing programs do a much better job of curving the line, no matter how close or far the sample points are. Some even let you give them a number to indicate the amount of smoothing to be done, accounting for jittery pens and hands. Where can I learn to do this stuff?
I know this is an old question but I had the same problem and I came with 2 different solutions:
The first approach is use two resolutions: One , when the user is inserting the path points connecting them with straight lines. Two , when the user finish the stroke delete the lines and draw the spline over. That should be smoother than the straight lines.
The second approach it is to smooth the new points with a weighted mean of the sampled points. So each time you get a new point [x1,y1] instead of painting it directly, you take the previous points [x2,y2] and create a new intermediate point with the weighted mean of the two points. The pseudocode could be something like:
newPoint = [x1,y1];
oldPoint = [x2,y2];
point2Paint = [(x1*0.3) + (x2*0.7), (y1*0.3) + (y2*0.7)];
oldPoint= newPoint;
Being 0.7 and 0.3 the coefficients for the weighted mean ( You can change them to get your desired smoothing :)
I hope this would help
UPDATE Dec 13: Here it is an article explaining different drawing methods, there are good concepts that can be applied (edge smoothing, bezier curves, smooth joints)
http://perfectionkills.com/exploring-canvas-drawing-techniques
I never had to implement these (only for academic purposes), but you may want to take a look at wikipedia's interpolation article.
Extracted from the article:
interpolation is a method of constructing new data points within the range of a discrete set of known data points.
In engineering and science one often has a number of data points, as obtained by sampling or experimentation, and tries to construct a function which closely fits those data points. This is called curve fitting or regression analysis. Interpolation is a specific case of curve fitting, in which the function must go exactly through the data points.
Hope it helps.