Code Or Idea For Structural Decomposition about point2skeleton

[clinplayer]

Hi, we gave illustrations for these two types of junctions in the latest version:

The non-manifold structure is like the shape "Y" where more than three line segments join together forming a vertex shared by more than three curves.

We simply use a part of the code from this work https://github.com/clinplayer/SEG-MAT
However, the implementation only for detecting the line-triangle joints and "Y" joints in this paper is trivial. You don't need to necessarily follow the code of SEG-MAT which is a bit complex.

[FishWoWater]

OK! Thanks

[FishWoWater]

Sorry to trouble you again.. How did you extract point cloud from original shapeNet data(you say virtual scanner in the paper, I speculate your practice is sth like uniform sampling)? I tried to sample 2k points uniformly from original mesh and compare with yours. I found that scale and global orientation of the point cloud are generally different. So I am wondering is there any pre-processing step in your pipeline? Should it be OK to use simple uniform sampling if I want to train on my custom dataset? Thanks!

[clinplayer]

Sorry to trouble you again.. How did you extract point cloud from original shapeNet data(you say virtual scanner in the paper, I speculate your practice is sth like uniform sampling)? I tried to sample 2k points uniformly from original mesh and compare with yours. I found that scale and global orientation of the point cloud are generally different. So I am wondering is there any pre-processing step in your pipeline? Should it be OK to use simple uniform sampling if I want to train on my custom dataset? Thanks!

Of course, you can directly sample on the meshes of shapenet. The reason we use a virtual scanner is that it can generate consistent normals. (Note we don't need normal vectors, but the competitive methods rely on them.)

The scale and orientation are not an issue; you just need to confirm that all your training data are normalized to the same distribution. For ours, I remember we normalize the coordinates of each shape to [-1, 1].

[FishWoWater]

Thanks! It worked for me. @clinplayer