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

How about training point clouds with different size? #28

Open
leihouyeung opened this issue Jul 27, 2023 · 1 comment
Open

How about training point clouds with different size? #28

leihouyeung opened this issue Jul 27, 2023 · 1 comment

Comments

@leihouyeung
Copy link

Hi, thanks for this fantastic work. In the paper, you said "The methods we describe here extend easily to the M = N case
because DGCNN, Transformer, and Softmax treat inputs as unordered sets. None requires X and Y to have the same length or a bijective matching."

Is there any implementation of the cases when M differs from N? Thanks.

@luisfmnunes
Copy link

luisfmnunes commented Sep 1, 2023

Usually what is done in the N x M case is to pad entries to a common value (example K > M > N) and then use the mask component of the Attention Module to neglect the effect of padded data.

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