This is our official implementation of the paper "Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting"!

by Jaewoo Jung^☔, Jisang Han^☔, Honggyu An^☔, Jiwon Kang^☔, Seonghoon Park^☔, Seungryong Kim^†

☔: Equal Contribution
†: Corresponding Author

We introduce a novel optimization strategy (RAIN-GS) for 3D Gaussian Splatting!

We show that our simple yet effective strategy consisted of sparse-large-variance (SLV) random initialization and progressive Gaussian low-pass filter control robustly guides 3D Gaussians to model the scene even when starting from random point clouds.

For further details and visualization results, please check out our paper and our project page.

We implement RAIN-GS above the official implementation of 3D Gaussian Splatting.
For environmental setup, we kindly guide you to follow the original requirements of 3DGS.

To train 3D Gaussian Splatting with our novel strategy (RAIN-GS), all you need to do is:

python train.py -s {dataset_path} --exp_name {exp_name} --eval --ours

For dense-small-variance (DSV) random initialization (used in the original 3D Gaussian Splatting), you can simply run with the following command:

python train.py -s {dataset_path} --exp_name {exp_name} --eval --DSV

To train with Mip-NeRF360 dataset, you can add argument --images images_4 for outdoor scenes and --images images_2 for indoor scenes to modify the resolution of the input images.

We would like to acknowledge the contributions of 3D Gaussian Splatting for open-sourcing the official codes for 3DGS!

If you find our work helpful, please cite our work as:

@article{jung2024relaxing,
  title={Relaxing Accurate Initialization Constraint for 3D Gaussian Splatting},
  author={Jung, Jaewoo and Han, Jisang and An, Honggyu and Kang, Jiwon and Park, Seonghoon and Kim, Seungryong},
  journal={arXiv preprint arXiv:2403.09413},
  year={2024}
}