Hi Sebastian, what are you trying to do with the 10 million+ point cloud? closest location to itself is going to be painful yes :) when the point clouds are reaching that high sometimes its best to have a similar pointcloud around but with far fewer points. like say 1%. Surprisingly it is a heck of a lot faster doing closest points lookup from cloud A(10mill+) to a lowres cloud B (100k+) than to itself.
So workflow is:- take original cloud A at 10mil+ duplicate and reduce down to Cloud B 1% with Ice nodes 'test random probability' and 'delete point' remember to freeze this cloud so the delete point is not live do all closest location lookup and additional processing on Cloud B when finished do closest location lookup cloud A to B to transfer results back to A Its not perfect solution for everything but in some cases with eg lighting pointclouds or working out densities in ICE it beats waiting around for those 10mill+ points to work themselves out! you may be able to do something cleverer with only the one high res pointcloud but for me this level of abstraction works out ok cheers Rob On 6 June 2012 09:26, Sebastian Kowalski <[email protected]> wrote: > hey list, > > i dont need to check all pointpositions for closest points, just a few. but > i cant figure out how to filter them out. > got this huge cloud (10.000.000+ points) and softimage processes neighbours > pts forever... > > the closest point node wont accept arrays, and i am afraid we cant get a > filtered set > > any ideas? > > thanks in advance > > > ps. ive tried to just calculate distance between all points with just array > resizing, this is fast, but i cant get near a million without skyrocketing > memory consumption, and therefore system freeze ups. > >
