if this is the wrong place to post about this please let me know. I wasn’t sure which category would fit best.
Hey everyone,
(Disclaimer: I don’t really have any background in machine learning and am approaching this from a visual effects TD perspective… so bear with me ^^)
I’m currently trying to develop a mesh fitting algorithm to be able to morph between two 3d meshes with different topologies using the chamfer function in pytorch3d.
I followed the tutorials 1 provided and managed to set everything up in my 3d software (Houdini). I was able to successfully morph between different meshes.
I now want to define certain key points / features which appear in the source as well as target mesh to be able to create a cleaner transition. (e.g. match eyes to eyes, nose to nose when fitting two different heads)
I found the “loss.chamfer_distance” 1 function takes weights as an argument but I don"t understand how to use them or whether this is even useful for my problem. Can someone explain what exactly this is supposed to do:
Explanation from pytorch3d docs:

weights – Optional FloatTensor of shape (N,) giving weights for batch elements for reduction operation.

I also wondered if it was possible to fit the mesh while maintaining topological symmetry of the source mesh or how I would go about implementing this.
I’m very thankful for any pointers on how to approach any of my two problems!
Thanks in advance!
Best,
Jakob