I have several sets of 3D data points. Each set represents dark features found on a spinning ball. These data points are produced by analyzing frames from a high speed camera so, there is some inherent error. I am attempting to track and label each point from frame to frame. When I use a ball with very distinct shapes or dots, this is not much of an issue since I know what I am looking for ahead of time and can brute force some identification/matching.
I'm trying to get away from using a specific pattern. This would require me to compare the 3D data sets from each frame to try and find points that exist in two subsequent frames (find valid points and ignore ones that appear to be noise) and then attempt to create matching point pairs.
I have tried some brute force methods of comparing distances, finding outliers, but have not had much luck without using a fixed or known pattern on the ball. I have run into many posts about this being an assignment problem and many people have suggested the Hungarian algorithm in other similar posts, but I have not been able to successfully implement that approach.
Any ideas of what I could try or some help with the Hungarian algorithm would be greatly appreciated.
This is an example of what this looks like when I use dots in a known pattern (3 dots as a right triangle) after I detect and sort points. Ignore the green dots, pink is what I would be dealing with.
