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This is a C# implementation of Philip J. Schneider's least-squares method for fitting Bézier curves to a set of input data points, as well as several helper routines/classes to work with Bézier curves. It's designed to work with several different Vector types: WPF, the SIMD-enabled vector types and Unity 3D, and is very simple to extend for use in other contexts. It's also high-performance and was profiled/micro-optimized to be quite fast without sacrificing API simplicity.


Say you have a bunch of input points like this (ie from a touchscreen or drawn by a mouse):


This seems to form a an "S" shape. However, there's a bit of jitter visible, so if you're displaying it you might want to smooth it out. More importantly, these points aren't evenly spaced and are generally quite difficult to work with programmatically. We need to transform this data into a format a computer can easily work with. The answer is Bézier curves.

How do we get a curve that approximates the data? The first step (optional, but highly recommended) is to remove some of the excess points. The most common method of doing this is the Ramer-Douglas-Pueker algorithm. After running it, the input data looks like:


A lot nicer! For some programs, this is enough to work with. For others, we need to fit some curves to the data, which is when we call up our friend Philip J. Schneider:


The colors denote 3 separate Bézier curves. These form a Composite Bézier curve with C1 continuity that approximates the input data. This library lets you do that in 2 lines of code:

List<Vector2> reduced = CurvePreprocess.RdpReduce(data, 2);   // use the Ramer-Douglas-Pueker algorithm to remove unnecessary points
CubicBezier[] curves = CurveFit.Fit(reduced, 8);              // fit the curves to those points

It also includes a WPF sample project so you can try this out for yourself and see what the parameters do, and how they affect the quality of the curves and the performance:


Neat, huh?

See and try for yourself

YouTube video of demo project in action

ZIP file with compiled demo project (NOTE: this requires .NET framework version 4.5 or higher)

Musings about performance with RyuJIT and SIMD (tl;dr: 3.4x speed-up with SIMD on!)

Getting the code to work with your project

This is not meant to be a library you compile into a DLL and link to (if you're using C#). Because there are so many different Vector types flying around, it's easiest just to copy the source code from the burningmime.curves/src folder directly into your project. You'll notice at the top of every file there, there's a very C-like construct:

using VECTOR = System.Windows.Vector;
using FLOAT = System.Double;
using VECTOR = System.Numerics.Vector2;
using FLOAT = System.Single;
#elif UNITY
using VECTOR = UnityEngine.Vector2;
using FLOAT = System.Single;
#error Unknown vector type -- must define one of SYSTEM_WINDOWS_VECTOR, SYSTEM_NUMERICS_VECTOR or UNITY

In your project properties, you can add one of the preprocessor symbols to the project depending on what you're targeting. Alternatively, you can simply do a #define at the top of each file. In Unity you should add UNITY to the global custom defines in the project settings -- see .

If you're using VB.Net, F#, or another .NET language, you'll need to compile it with the correct Vector type and reference the compiled DLL.

You can very easily add support for new vector types (assuming it has overloaded operators) by modifying VectorHelper.cs. For example, SharpDX.Vector2 and Microsoft.Xna.Framework.Vector2 are trivial to add since they use the same interface as System.Numerics.Vector2. I haven't looked into WinRT much but it might have a vector type similar to System.Windows.Vector (WPF).

Using the code

See the code documentation for usage info on the specific functions. The most important ones are the ones demonstrated above -- CurvePreprocess.RdpReduce to simplify input data and CurveFit.Fit to fit curves to the data. You don't need to pre-process the input data before calling CurveFit.Fit, but the input data MUST NOT contain any repeated points (one point after another that's exactly the same) or undefined behavior can occur (probably returning some stuff with NaN values). Both the RDP and Linearize methods of pre-processing will remove duplicates, but you can call CurvePreprocess.RemoveDuplicates if you're not doing wither of the other pre-processing methods and are concerned there might be duplicates.

The parameters are tuneable based on your use case. I recommend playing around with the sample app for a bit to get a feel for exactly how the parameters and pre-processing methods work. This will give a much better explanation than I could. Note the red text in the bottom-right: this shows you how long the fit operation took, which will help you make decisions base don performance. Generally, the "fit error" parameter doesn't make nearly as much difference in terms of performance as the number of input points does. Fit error instead helps determine how smooth the generated curves will be.

Included is also a CurveBuilder class which lets you incrementally add points and update curves as they come in. It uses its own pre-processing method (ignoring all points that are too close the previous one, basically) and splits the final curve when it no longer fits the input data. This is useful if you want to build curves "as you go" rather than fitting all at once, but isn't suitable for displaying to the user without some massaging first since it's still a bit jumpy.

Finally, there's a Spline class, which isn't actually a spline, but instead just a simple way to re-parameterize a composite curve with C1 continuity so that it can be sampled in a linear fashion. That is, if you call Spline.Sample(0.5), you'll get a point roughly halfway down the spline. This lets you animate things "moving along" the curves without them speeding up/slowing down randomly, and is also helpful for rendering it. See this for an explanation of the problem that this class is helpful in solving. The Spline class is a fair bit simpler than David Eberly's Method, but with the advantage that it's a faster after the initial setup time and less prone to floating point instability. It uses linear interpolation to find a good parameter value then samples a specific point on the curve.

Enabling SIMD

I'll just leave this here:


This started out as a straight port from here:

The Ramer-Douglas-Pueker code is an optimized version of the C# code here:

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