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Nik-V9 avatar Nik-V9 commented on September 4, 2024 1

Hi, Thanks for trying out our code and for this amazing enhancement!

  1. Please open a pull request and feel free to add an example use of the .ply format on the README. Happy to incorporate it ourselves if you think that's better.
  2. Yes, we currently use isotropic scaling. It should be easy for us to support anisotropic Gaussians in Pytorch (we need to add a few configs across the code, adding back our old code). I'll add this to our V2 list. We don't see any performance discrepancies for isotropic vs anisotropic. The nice thing about isotropic is that it has fewer parameters, so if we implement the CUDA operations accordingly, it should be faster and have lower memory. Hopefully, we plan to have custom CUDA implementations soon.

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ironjr avatar ironjr commented on September 4, 2024

Thank you for your thoughtful consideration. I have opened a pull request. #29

By drag and dropping .ply file generated in the same directory as params.npz into any GS viewers, for example, https://playcanvas.com/super-splat you will visualize / edit the Gaussians generated by your algorithm.

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Buffyqsf avatar Buffyqsf commented on September 4, 2024

Hi, Thanks for trying out our code and for this amazing enhancement!

  1. Please open a pull request and feel free to add an example use of the .ply format on the README. Happy to incorporate it ourselves if you think that's better.
  2. Yes, we currently use isotropic scaling. It should be easy for us to support anisotropic Gaussians in Pytorch (we need to add a few configs across the code, adding back our old code). I'll add this to our V2 list. We don't see any performance discrepancies for isotropic vs anisotropic. The nice thing about isotropic is that it has fewer parameters, so if we implement the CUDA operations accordingly, it should be faster and have lower memory. Hopefully, we plan to have custom CUDA implementations soon.

Hey, thank you for your work. I am reading the code these days. I want to know how do you imply isotropic in code. Is that because you use torch.tile() to copy scale? So that all three directions' scales will have the same grad?

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Nik-V9 avatar Nik-V9 commented on September 4, 2024

Hi, Yes. Since the Gaussians are isotropic (spherical), we only model one parameter for scale across x, y & z instead of using 3 parameters.

As you mentioned, currently, we pass the Gaussians using a tile operation to be compatible with the anisotropic CUDA rendering from the official 3DGS code, as shown here:

'scales': torch.exp(torch.tile(params['log_scales'], (1, 3))),

Furthermore, the isotropic Gaussians are defined here:

'log_scales': torch.tile(torch.log(torch.sqrt(mean3_sq_dist))[..., None], (1, 1)),
The dimension of the log_scales is [num_gaussians, 1].

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Buffyqsf avatar Buffyqsf commented on September 4, 2024

@Nik-V9 I got it. Thanks!

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