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Prior-driven initialization of Gaussian Splatting

This is the implementation of prior-driven initialization described in "3.2.1 Single frame initialization" in "GFlow: Recovering 4D World from Monocular Video".

This initialization method, which takes an image and its corresponding depth as input, can achieve faster 3DGS reconstruction compared to random initialization.

Method RGB Depth Centers
Prior-init (500steps)
training_rgb_prior.mp4
training_depth_prior.mp4
training_center_prior.mp4
Random-init (5000 steps)
training_rgb_random.mp4
training_depth_random.mp4
training_center_random.mp4

Usage

  1. Install following msplat's instructions.
  2. run python tutorials/run.py and check the results in the logs folder.

Acknowledgment

We thank the msplat team for re-implementing 3DGS in a more developer-friendly way. We also salute the original 3DGS team for their seminal work.

If you find this initialization method helpful, please consider to cite:

@article{wang2024gflow,
  title={GFlow: Recovering 4D World from Monocular Video},
  author={Wang, Shizun and Yang, Xingyi and Shen, Qiuhong and Jiang, Zhenxiang and Wang, Xinchao},
  journal={arXiv preprint arXiv:2405.18426},
  year={2024}
}