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This paper introduced a new conceptual model for how to view the process of gaussian splat optimization and then used that to guide several changes to the original algorithm, improving results while simplifying the various "clone", "split", and "prune" operations into a single "relocation" type.
This is a brief summary from the paper of the changes it introduces:
To summarize, our contributions are:
• we reveal the link between 3DGS and MCMC sampling, leading to a simpler optimization; • we replace the heuristics in 3D Gaussian Splatting with a principled relocation strategy;
• we introduce regularizer to encourage parsimonious use of Gaussians;
• we improve robustness to initialization;
• we provide higher rendering quality.
The text was updated successfully, but these errors were encountered:
https://ubc-vision.github.io/3dgs-mcmc/
This paper introduced a new conceptual model for how to view the process of gaussian splat optimization and then used that to guide several changes to the original algorithm, improving results while simplifying the various "clone", "split", and "prune" operations into a single "relocation" type.
This is a brief summary from the paper of the changes it introduces:
The text was updated successfully, but these errors were encountered: