tosca shape

[therateful]

Hi, I am trying to subdivide the shapes in the Tosca dataset, as shown in Table 2 in the paper, but I found that many of the shapes have boundaries, how did you subdivide these shapes and get the results?

[therateful]

In addition, the Metro tool outputs two mean distances, which is used as the average surface distance

[HTDerekLiu]

Re: TOSCA
I closed the mesh (see attached). Alternatively, you could make it work by doing some padding too.
tosca_closed.zip

Re: Metro
Sorry it has been a while, I couldn't recall what are the numbers and which one I used.

[therateful]

thank you very much

[therateful]

Re: TOSCA I closed the mesh (see attached). Alternatively, you could make it work by doing some padding too. tosca_closed.zip

Re: Metro Sorry it has been a while, I couldn't recall what are the numbers and which one I used.

I'm sorry, but I might have one more question for you.
I performed mesh simplification on the original meshes you shared, then subdivided them with neural subdivision, and finally quantitative analysis with Metro tools, but got poor results on some models.
and I have observed that the simplified mesh model is a little bad, so could you please share your simplified mesh?

[therateful]

Or am I using the Metro tool incorrectly? I used the simplest assessment, "Metro A.obj B.obj."

[HTDerekLiu]

Based on the file name, this should be the ones I was using (not 100% sure, it has been a while)

• tosca test data: https://drive.google.com/file/d/1BKbFRmXMToMMr0o6zr1j3AYbAruuOcLD/view?usp=drive_link
• tosca train data: https://drive.google.com/file/d/1hYR-MtOmNiXyE6I5cUQgrm55twCeXcpI/view?usp=drive_link

[therateful]

Based on the file name, this should be the ones I was using (not 100% sure, it has been a while)

• tosca test data: https://drive.google.com/file/d/1BKbFRmXMToMMr0o6zr1j3AYbAruuOcLD/view?usp=drive_link
• tosca train data: https://drive.google.com/file/d/1hYR-MtOmNiXyE6I5cUQgrm55twCeXcpI/view?usp=drive_link

Thanks for sharing, but it seems to require additional permissions to access this connection

[therateful]

Based on the file name, this should be the ones I was using (not 100% sure, it has been a while)

• tosca test data: https://drive.google.com/file/d/1BKbFRmXMToMMr0o6zr1j3AYbAruuOcLD/view?usp=drive_link
• tosca train data: https://drive.google.com/file/d/1hYR-MtOmNiXyE6I5cUQgrm55twCeXcpI/view?usp=drive_link

I used the coarse mesh you provided and subdivided it by neural subdivision, but the Hausdorff distance calculated by Metro between the prediction model and the ground-truth model of cat is still 134.xxxx >>>2.08 (the figure in paper ).
However, when I work with other models, non-TOSCA models, the values are normal.Is there a problem with the model of the Tosca dataset？Or am I using the Metro tool incorrectly?

[HTDerekLiu]

I guess the mesh scale may be incorrect? Here are some pre-trained weights on centaur and subdivision results for you to debug.
https://drive.google.com/file/d/197Nm5Z6uI6uHmf-xWXWH0ljXrUuwobzk/view?usp=sharing

[therateful]

I guess the mesh scale may be incorrect? Here are some pre-trained weights on centaur and subdivision results for you to debug. https://drive.google.com/file/d/197Nm5Z6uI6uHmf-xWXWH0ljXrUuwobzk/view?usp=sharing

You are right, it is a matter of scale, but how to adjust the scale correctly?

[therateful]

I guess the mesh scale may be incorrect? Here are some pre-trained weights on centaur and subdivision results for you to debug. https://drive.google.com/file/d/197Nm5Z6uI6uHmf-xWXWH0ljXrUuwobzk/view?usp=sharing

And I'm a little confused, why is there such a big difference between the scale of subdivision output mesh and the ground-truth mesh？

[HTDerekLiu]

The method is not scale invariant so we normalize the mesh to a unit box. In the previous data, *_s0.obj, *_s1.obj, *_s2.obj are the subdivision results. *_target.obj are the target ground truth shapes used to compute losses.

[therateful]

Ok, I see. Thank you for your help.

[therateful]

Hello, I would like to raise a question about bijective mappings in Successive self-parameterization. When establishing the mapping g from subdivision mesh to coarse mesh, why use the barycentric coordinates to encode the mapping relationship instead of directly using the midpoint interpolation method to represent the mapping relationship?

[HTDerekLiu]

We estimate the mapping between the subdivided mesh and the coarse mesh using exactly the idea you mentioned -- the mid point relationship. The barycentric coordinates is used to represent the mapping between the input high resolution mesh and the coarse mesh.