Support MCMC Strategy in Splatfacto

[yufengzh]

This PR adds the MCMC strategy for Splatfacto. To use, specify the following in the CLI argument:

ns-train splatfacto --pipeline.model.strategy=mcmc

[abrahamezzeddine]

Hello,

I just tried this and it seems to work. I am however wondering about the huge splats being created and large splats going straight through the model, which creates the illusion of hue artifacts inside the area of interest. See image below;

Is that an inherent problem with mcmc in general? Another example with depth map to show how huge splats are just "going through" the model

They are several magnitudes larger than sparse cloud itself.

[kerrj]

Thanks for the PR! I believe this is missing the opacity + scale regularization in the original MCMC paper, do you think you could add those? that might be a reason for these large gaussians during training

[yufengzh]

Does gsplat have those regularization terms implemented or not yet?

[CameronFraser]

@yufengzh they are not part of gsplat, there is an example here for how to implement: https://github.com/nerfstudio-project/gsplat/blob/main/examples/simple_trainer.py#L661

  # regularizations
  if cfg.opacity_reg > 0.0:
      loss = (
          loss
          + cfg.opacity_reg
          * torch.abs(torch.sigmoid(self.splats["opacities"])).mean()
      )
  if cfg.scale_reg > 0.0:
      loss = (
          loss
          + cfg.scale_reg * torch.abs(torch.exp(self.splats["scales"])).mean()
      )