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This repository has been archived by the owner on Dec 13, 2023. It is now read-only.
I'm curious, have you thought about the correct way of extracting the isosurfaces from the trained implicit function?
For vanilla NeRF model it is possible to extract the level surface at two scales using separately trained coarse and fine networks. Here, as far as I understand, it is possible to extract the level surface at an arbitrary scale, and for that I could just query the network with a positional encoding obtained for a desired point x and some manually selected variance, which determines the scale.
Does this approach makes sense to you, or are there some reasons why it could fail?
The text was updated successfully, but these errors were encountered:
Hello!
Thanks for sharing this awesome work! :)
I'm curious, have you thought about the correct way of extracting the isosurfaces from the trained implicit function?
For vanilla NeRF model it is possible to extract the level surface at two scales using separately trained coarse and fine networks. Here, as far as I understand, it is possible to extract the level surface at an arbitrary scale, and for that I could just query the network with a positional encoding obtained for a desired point x and some manually selected variance, which determines the scale.
Does this approach makes sense to you, or are there some reasons why it could fail?
The text was updated successfully, but these errors were encountered: