I need to match two sets of 3D points, however the number of points in each set can be different. It seems that most algorithms are designed to align images and trimmed to work with hundreds of thousands of points. My case are 50 to 150 points in each of the two sets.

So far I have acquainted myself with Iterative Closest Point and Procrustes Matching algorithms. Implementing Procrustes algorithms seems like a total overkill for this small quantity. ICP has many implementations, but I haven't found any readily implemented version accounting for the so-called "outliers" - points without a matching pair.

Besides the implementation expense, algorithms like Fractional and Sparse ICP use some statistics information to cancel points that are considered outliers. For series with 50 to 150 points statistic measures are often biased or statistic significance criteria are not met.

I know of Assignment Problem in linear optimization, but it is not suitable for cases with unequal sets of points.

Are there other, small-scale algorithms that solve the problem of matching 2 point sets? I am looking for algorithm names, scientific papers or C++ implementations. I need some hints to know where to start my search.