When would we want to use Catmull-Rom interpolation over other types of interpolation? For instance, hermite spline interpolation?
lycorisradiatu
In terms of using Catmull-Rom interpolation over others, it seems Catmull-Rom interpolation is often preferred when you need local control and simplicity. It is relatively easy to compute and, since each curve segment depends only on its neighboring control points, it is well-suited for interactive applications where you want to manipulate individual control points.
Songbird94
I had the same question and it seems hermite interpolation gives more precise control over the shape of the curve while Catmull-Rom is more flexible and especially useful generating paths or motion trajectories.
adam2451
Catmull-Rom also seems to be used when we lack the information of the derivative over the data we are analyzing. Otherwise it is basic Cubic Hermite Interpolation.
adam2451
This also leads me to wonder if Catmull-Rom would be a bad choice for certain data sets, since the linear estimate of the derivative could be worse and worse the farther consecutive points get from each other.
When would we want to use Catmull-Rom interpolation over other types of interpolation? For instance, hermite spline interpolation?
In terms of using Catmull-Rom interpolation over others, it seems Catmull-Rom interpolation is often preferred when you need local control and simplicity. It is relatively easy to compute and, since each curve segment depends only on its neighboring control points, it is well-suited for interactive applications where you want to manipulate individual control points.
I had the same question and it seems hermite interpolation gives more precise control over the shape of the curve while Catmull-Rom is more flexible and especially useful generating paths or motion trajectories.
Catmull-Rom also seems to be used when we lack the information of the derivative over the data we are analyzing. Otherwise it is basic Cubic Hermite Interpolation.
This also leads me to wonder if Catmull-Rom would be a bad choice for certain data sets, since the linear estimate of the derivative could be worse and worse the farther consecutive points get from each other.