3dgs-mcmc's Introduction

3D Gaussian Splatting as Markov Chain Monte Carlo

BibTeX

@article{kheradmand20243d,
  title={3D Gaussian Splatting as Markov Chain Monte Carlo},
  author={Kheradmand, Shakiba and Rebain, Daniel and Sharma, Gopal and Sun, Weiwei and Tseng, Jeff and Isack, Hossam and Kar, Abhishek and Tagliasacchi, Andrea and Yi, Kwang Moo},
  journal={arXiv preprint arXiv:2404.09591},
  year={2024}
}

How to install

Please refer to Original 3DGS code base for installation instructions. Make sure to reinstall diff-gaussian-rasterization with the following command on an available 3DGS environment as this library has been modified:

pip install submodules/diff-gaussian-rasterization

How to run

Running code is similar to the Original 3DGS code base with the following differences:

You need to specify the maximum number of Gaussians that will be used. This is performed using --cap_max argument. The results in the paper uses the final number of Gaussians reached by the original 3DGS run for each shape.
You need to specify the scale regularizer coefficient. This is performed using --scale_reg argument. For all the experiments in the paper, we use 0.01.
You need to specify the opacity regularizer coefficient. This is performed using --opacity_reg argument. For Deep Blending dataset, we use 0.001. For all other experiments in the paper, we use 0.01.
You need to specify the noise learning rate. This is performed using --noise_lr argument. For all the experiments in the paper, we use 5e5.
You need to specify the initialization type. This is performed using --init_type argument. Options are random (to initialize randomly) or sfm (to initialize using a pointcloud).

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