Collaboration
theFilipko opened this issue · 8 comments
Hello, thanks for sharing this implementation, it is awesome.
I am also implementing this and "mip-nerf 360" into my codebase. Would you like to work on it together?
I had a look at your latest commit and actually this
far_inv = 1 / far
near_inv = 1 / near
t_samples = far_inv * t_samples + (1 - t_samples) * near_inv
t_samples = 1 / t_samples
equals to this
t_samples = 1. / (1. / near * (1. - t_samples) + 1. / far * t_samples)
@theFilipko hey, thanks for your advice! It's my pleasure, Have you finished mip-nerf 360?
I am in the middle of it. Please, contact me here filip.hendrichovsky@gmail.com
Hello, have you finished mip-nerf 360? I look forward to your implementation.
@HangXiao-97 if you would like to participate in the implementation, contact me on that email :)
Hey! I am wondering if the mip-nerf 360 implementation is complete?
If not and if functionality and results have changed, can you refer to a functioning mip nerf from a previous commit?
Thanks!
Hey, thanks for reaching out. There has been a code release https://github.com/google-research/multinerf Check this out
Hey, I have checked that out but unfortunately, it's in jax, and flowing gradients between our pytorch framework and jax is an extra overhead and causes a lot of issues which is why your framework is very useful. In either case, It seems that your implementation works for mipnerf right? i can implement the mip-nerf360 on top of it if the base works