NeRFtoGSandBack

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NeRFtoGSandBack

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# From NeRFs to Gaussian Splats, and Back
This is the implementation of [From NeRFs to Gaussian Splats, and Back](https://arxiv.org/abs/2405.09717); An efficient procedure to convert back and forth between NeRF and GS, and thereby get the best of both approaches. New dataset in the paper can be downloaded from [this google drive link](https://drive.google.com/drive/folders/1xvbONL4EVgHxaHMsV101455l_jNgyaUM?usp=sharing). This is now a part of [nerfstudio methods](https://docs.nerf.studio/nerfology/methods/nerf2gs2nerf.html) and you can use it directly from nerfstudio!

https://github.com/grasp-lyrl/NeRFtoGSandBack/assets/69362937/7f1d8f55-79f4-4090-9ba7-f5d1b9a17edb

## Installation
This repository follows the nerfstudio method [template](https://github.com/nerfstudio-project/nerfstudio-method-template/tree/main)

### 0. Install Nerfstudio dependencies
Please follow the [installation guide](https://docs.nerf.studio/quickstart/installation.html)  to create an environment and install dependencies including PyTorch and tiny-cuda-nn.

**As mentioned in this [issue](https://github.com/grasp-lyrl/NeRFtoGSandBack/issues/4), the code currently works with only the older version of nerfstudio and gsplat. Please install nerfstudio and gsplat following:**
```
pip uninstall gsplat nerfstudio -y

pip install gsplat==0.1.3
pip install git+https://github.com/nerfstudio-project/nerfstudio.git@v1.0.1
```

### 1. Install the repository
Clone and navigate into this repository. Run the following commands:

`pip install -e nerfsh`

and

`pip install -e nerfgs`.

Finally, run `ns-install-cli`.

### 2. Check installation
Run `ns-train --help`. You should be able to find two methods, `nerfsh` and `nerfgs`, in the list of methods.

## Downloading data
You could download the Giannini-Hall and aspen datasets from [this google drive link](https://drive.google.com/drive/folders/19TV6kdVGcmg3cGZ1bNIUnBBMD-iQjRbG). Our new dataset (Wissahickon and Locust-Walk) can be downloaded from [this google drive link](https://drive.google.com/drive/folders/1xvbONL4EVgHxaHMsV101455l_jNgyaUM?usp=sharing).

## NeRFGS: NeRFs to Gaussian Splats
### Training NeRF-SH
Run the following command for training. Replace `DATA_PATH` with the data directory location.

`ns-train nerfsh --data DATA_PATH --pipeline.model.camera-optimizer.mode off `

To train on Wissahickon or Locust-Walk dataset, you need to add `nerfstudio-data --eval-mode filename` to properly split training and validation data, i.e.,

`ns-train nerfsh --data DATA_PATH --pipeline.model.camera-optimizer.mode off nerfstudio-data --eval-mode filename`

### NeRFGS: Converting NeRF-SH to Guassian splats
Replace `CONFIG_LOCATION` with the location of config file saved after training.

`ns-export-nerfsh --load-config CONFIG_LOCATION --output-dir exports/nerfgs/ --num-points 2000000 --remove-outliers True --normal-method open3d --use_bounding_box False`

### Visualize converted Gaussian splats
Replace `DATA_PATH` with the data directory location. You also need to add `nerfstudio-data --eval-mode filename` if train on Wissahickon or Locust-Walk.

`ns-train nerfgs --data DATA_PATH --max-num-iterations 1 --pipeline.model.ply-file-path exports/nerfgs/nerfgs.ply`

### Fintuned NeRFGS 
We reduces the learning rate for finetuning. You also need to add `nerfstudio-data --eval-mode filename` if train on Wissahickon or Locust-Walk.

`ns-train nerfgs --data DATA_PATH --pipeline.model.ply-file-path exports/nerfgs/nerfgs.ply --max-num-iterations 5000 --pipeline.model.sh-degree-interval 1 --pipeline.model.warmup-length 100 --optimizers.xyz.optimizer.lr 0.00001 --optimizers.xyz.scheduler.lr-pre-warmup 0.0000001 --optimizers.xyz.scheduler.lr-final 0.0000001 --optimizers.features-dc.optimizer.lr 0.01 --optimizers.features-rest.optimizer.lr 0.001 --optimizers.opacity.optimizer.lr 0.05 --optimizers.scaling.optimizer.lr 0.01 --optimizers.rotation.optimizer.lr 0.0000000001 --optimizers.camera-opt.optimizer.lr 0.0000000001 --optimizers.camera-opt.scheduler.lr-pre-warmup 0.0000000001 --optimizers.camera-opt.scheduler.lr-final 0.0000000001`

## GSNeRF: Gaussian Splats to NeRFs

### Scene modification
To remove a bounding box of Gaussians in nerfgs, we add the following argument in the ns-train call:

`ns-train nerfgs --data DATA_PATH --max-num-iterations 1 --pipeline.model.ply-file-path exports/nerfgs/nerfgs.ply --pipeline.model.bottom_remove_box_corner "(MIN_X, MIN_Y, MIN_Z)" --pipeline.model.top_remove_box_corner "(MAX_X, MAX_Y, MAX_Z)"`

These arguments create a 3D box spanning from the minimum x, y, z values to the maximum x, y, z value and removes Gaussians with means located inside the box.

### Rendering new training images
In the new dataset, training images are rendered from splats. Replace `CONFIG_LOCATION` with the location of config file saved after training.

`ns-nerfgs-render --load-config CONFIG_LOCATION --render-output-path exports/splatting_data --export-nerf-gs-data`

### GSNeRF: Training on new training images
`ns-train nerfsh --data exports/splatting_data --pipeline.model.camera-optimizer.mode off nerfstudio-data --eval-mode filename`

## Extending the method
The conversion from NeRF to GS has inefficiency as mentioned at the discussion section of the paper. We welcome your efforts to reduce the inefficiency! The code for conversion is mainly in `nerfsh/nerfsh/nerfsh_exporter.py`.

## Bibtex
```
@misc{he2024nerfs,
      title={From NeRFs to Gaussian Splats, and Back}, 
      author={Siming He and Zach Osman and Pratik Chaudhari},
      year={2024},
      eprint={2405.09717},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}
```

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