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In the experimental part of your paper, you mentioned that the experimental configuration of feature tri-planes + hash tables + single NeRF was used, which is the fifth line of Table 2.
I'd like to know how you get the feature tri-planes because using single-shared-MLP seems to mean that you don't need to use the 2D CNN decoder, so how do you build the feature tri-planes?
Thank you very much for your answer.
The text was updated successfully, but these errors were encountered:
Thank you very much for your timely reply.
So in this experimental setup, what you ultimately need to store is a 2D CNN decoder, latent vectors, hash tables, and a standard NERF-like MLP network?
In the experimental part of your paper, you mentioned that the experimental configuration of feature tri-planes + hash tables + single NeRF was used, which is the fifth line of Table 2.
I'd like to know how you get the feature tri-planes because using single-shared-MLP seems to mean that you don't need to use the 2D CNN decoder, so how do you build the feature tri-planes?
Thank you very much for your answer.
The text was updated successfully, but these errors were encountered: