question concerning labeling the data

[hamzam0n]

Hello, thanks for your great work and offered code.

i'm working on a school project and i want to use the SUBDIVNET code to train my data, but there are some points i did not understand..

in each .obj file there are 3 indexes:

• raw_labels: each item here represents a label of a face in the original data.
• sub_labels: each item here represents a label of a face in the data after remeshing it (after applying datagen_maps.py on it )
• raw_to_sub: this i don't understand what does it represent, and what is its role

another thing i didn't understand: length of raw_labels and sub_labels is 13776 but number of faces in each object is 16384. so if raw_labels and sub_labels are representing the label of each face why they don't have same length as faces.

so can you please explain to me how can i preprocess step by step my data to before feeding it to the subdivnet? how can i make each object have the exact number of faces as in your input data (16384) and how to label my data, and if you can recommend me a tool i can use!

thank you in advance

[lzhengning]

Sorry for the late response.

https://github.com/lzhengning/SubdivNet/issues/9#issuecomment-885535277 may answer the first question.

The later problem should not occur. Because the length of raw_labels should be the same with the number of faces in the raw mesh, and length of sub_labels should be that of the remeshed shape, which is the direct input of SubdivNet. In your case, there ought to be 16384 sub_labels.

Please have another look at the json file. You can further provide me the file if I am wrong.

If you want to remesh your own data, please see the corresponding section in README. The paper also provides a detailed discussion about the remeshing algorithm. To precess shapes to 16384 (= 256 * 4^3) faces, the base mesh should be 256, and the subdivision depth is 3.

[hamzam0n]

thank you for making things clear things are clear now, that's close the issue