billrobertson42

04-28-2005, 01:55 AM

Ok, I'm sure I'm not the first with this idea...

I'm working on a plug-in to align points that are "close enough" to each other points from an arbitrary set of points. For example, you're working on a model that consists of multiple mesh objects and you want to make sure that the edges of the various meshes line up exactly. You select the points on the matching edges, and if they're within a given tolerence the postion of those two points will be averaged.

In order to avoid having to compare the location of every point to every other point in the set I want to group them so I only have to compare the locations of those that are close to each other. My cunning plan is to create a sparse matrix and the location of each point will be specified by the original coordinate divided by the tolerence. And then traverse through the sparse matrix looking for clusters of points that might be close enough and then average them.

Does this make sense?

I'm working on a plug-in to align points that are "close enough" to each other points from an arbitrary set of points. For example, you're working on a model that consists of multiple mesh objects and you want to make sure that the edges of the various meshes line up exactly. You select the points on the matching edges, and if they're within a given tolerence the postion of those two points will be averaged.

In order to avoid having to compare the location of every point to every other point in the set I want to group them so I only have to compare the locations of those that are close to each other. My cunning plan is to create a sparse matrix and the location of each point will be specified by the original coordinate divided by the tolerence. And then traverse through the sparse matrix looking for clusters of points that might be close enough and then average them.

Does this make sense?