Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Reconstruct 3D image #4

Open
tanquer opened this issue Jul 31, 2019 · 8 comments
Open

Reconstruct 3D image #4

tanquer opened this issue Jul 31, 2019 · 8 comments

Comments

@tanquer
Copy link

tanquer commented Jul 31, 2019

Can I use these descriptors to reconstruct to 3D image? How to do that?

@mkazhdan
Copy link
Owner

You would like to get a voxel grid?

@tanquer
Copy link
Author

tanquer commented Jul 31, 2019

Yes, I want to get the voxel grid from descriptors.

@mkazhdan
Copy link
Owner

So you can't get the voxel grid from the descriptor (as the process making the descriptor rotationally-invariant loses information). However, if you're interested in getting the voxel grid used to generate the shape descriptors, that is possible.

For example, in the file ShapeDescriptor.cpp, the voxel grid is available within the CubeGrid object "gedt" after line 107. (The command "GaussianEDT" computes the Gaussian Euclidean Distance Trasnform, gedt, from the rasterization of the triangle mesh, grid.)

@tanquer
Copy link
Author

tanquer commented Aug 1, 2019

Thank you very much! So do you know any possible 3D descriptor which not only can describe the shape of 3D image but also can reconstruct to the voxel grid? I have found the 3D Zernike descriptor (http://cg.cs.uni-bonn.de/project-pages/3dsearch/downloads.html) which can do that, but it seems that their code didn't work correctly and I don't know how they voxelize the object.

@mkazhdan
Copy link
Owner

mkazhdan commented Aug 1, 2019

Is there a reason not to use the voxel grid directly as the descriptors? I understand that it's not rotation-invariant, but rotation-invariance tends to come with a loss of information (even if you're using Zernike moments).

@tanquer
Copy link
Author

tanquer commented Aug 2, 2019

Well, my purpose is to use some parameters to describe the shape of some images and try to find the distribution of these parameters for future imitated generating. In 2D image, we can easily use Fourier descriptor but in 3D image, it seems be much more difficult.

@tanquer
Copy link
Author

tanquer commented Aug 2, 2019

The problem that I can't just use the voxel grid is that, if we generate every voxel by the probability, we can not promise the boundary of the final object is continuous. Maybe it will be the scattered point map. What's more, I think rotation-invariance is not important for my purpose, so if I do not need this function, can I reach my point?

@tanquer
Copy link
Author

tanquer commented Aug 2, 2019

I know that nowadays, many new techs such as 3D GAN can do it directly, but I don't think we have enough 3D images for training this project.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants