I have a set of 3D points (cartesian coordinates). I want to find the best fit plane. As I understand, there are many algorithms to get a best fit plane. One of them is this by Dan Couture.
This fits a plane by calculating the least distances to each point. This is what I was using all the while.
Now, I want to fit this plane such that noise is ignored. I.e., if the normal distance from the point to this plane is beyond a certain value, I want to ignore it. This way all the noisy points in my data set are disregarded to fit plane. How can this be done?
When I say "plane fit", it means I want the equation of that plane.
Please reply at the earliest.