Seeking faster objective mapping of noisy, irregularly spaced data

If you have noisy data sampled at irregular x,y locations, you can use objective mapping to make a smooth map of the results. I have used Kirill Pankratov's nice implementation objmap (described here, code here) for many years. I like it better than griddata because you choose the x- and y-scales over which datapoints "influence" the final map, and the amount of error to allow at your measurement locations. Now I have a new application that requires making many such maps, and speed is an issue. I wanted to check if there is a more recent Matlab implementation that runs faster or another fast mapping technique meant for noisy sparse data. I tried using the Matlab profiler on the objmap function but there were no obvious bottlenecks. [I found barnesn on the File Exchange which works fine, but it seems to be slower than objmap.] Thanks for any ideas.

[KAE]

I just learned about scatteredinterpolant and will give it a try. Some commentary is here, https://blogs.mathworks.com/loren/2015/07/01/natural-neighbor-a-superb-interpolation-method/ (sorry, I can't seem to insert the link).