Questions about Multi-type Joint Training in 2D-3D about 3d-iwgan HOT 4 CLOSED

I guess I didn't set it up well for joint training for this part. Neither of these would work though because that parameter is supposed to be a string not a list. I would suggest either using -d 'data/voxels/*/' and -i 'data/overlays/*/' or using Regex to select for the classes you want. I don't know what joint refers to here, maybe you altered the code?

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I guess I didn't set it up well for joint training for this part. Neither of these would work though because that parameter is supposed to be a string not a list. I would suggest either using -d 'data/voxels//' and -i 'data/overlays//' or using Regex to select for the classes you want. I don't know what joint refers to here, maybe you altered the code?

Hi，I've changed the list to a string, and the ‘joint’ folder means to put together six voxel files downloaded and converted from the shapenet dataset,because I saw in your paper about the accuracy of joint training of multi-class data and so on, hoping to be able to use your code for multi-class training.
The main problem is that I don't understand the difference between training six class of data and the implementation of training single class like ‘chair’ based on code 20-VAE-3D-IWGAN.py.
Or do I mix six class of dataset to voxels folder and six kinds of image files together in overlays?
I don't know if it's right for many types of training. Six class of data can be directly mixed together without labels.
In addition, we need to generate the voxel model of 202020 for the operation of 2D-3D. I can't understand the reason, but I hope you can help me solve these problems. Thank you.

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yes you just mix everything together, but should split by class when evaluating. I generate at 20/20/20 resolution because that is the resolution used to evaluate in the 3D GAN paper.

from 3d-iwgan.

yes you just mix everything together, but should split by class when evaluating. I generate at 20/20/20 resolution because that is the resolution used to evaluate in the 3D GAN paper.

Thank you for your answer. It helps me a lot。

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