SAGA

The official implementation of SAGA (Segment Any 3D GAussians).

Installation

The installation of SAGA is similar to 3D Gaussian Splatting.

git clone [email protected]:Jumpat/SegAnyGAussians.git

or

git clone https://github.com/Jumpat/SegAnyGAussians.git

Then install the dependencies:

conda env create --file environment.yml
conda activate gaussian_splatting

In default, we use the public ViT-H model for SAM. You can download the pre-trained model from here and put it under ./third_party/segment-anything/sam_ckpt.

Prepare Data

The used datasets are 360_v2, nerf_llff_data and LERF.

The data structure of SAGA is shown as follows:

./data
    /360_v2
        /garden
            /images
            /images_2
            /images_4
            /images_8
            /sparse
            /features
            /sam_masks
            /mask_scales
        ...
    /nerf_llff_data
        /fern
            /images
            /poses_bounds.npy
            /sparse
            /features
            /sam_masks
            /mask_scales
        /horns
            ...
        ...
    /lerf_data
        ...

Since we need the pre-trained 3D-GS model for mask scales extraction, the first step is to train the 3D Gaussians:

Pre-train the 3D Gaussians

We inherit all attributes from 3DGS, more information about training the Gaussians can be found in their repo.

python train_scene.py -s <path to COLMAP or NeRF Synthetic dataset>

Prepare data

Then, to get the sam_masks and corresponding mask scales, run the following command:

python extract_segment_everything_masks.py --image_root <path to the scene data> --sam_checkpoint_path <path to the pre-trained SAM model> --downsample <1/2/4/8>
python get_scale.py --image_root <path to the scene data> --model_path <path to the pre-trained 3DGS model>

Note that sometimes the downsample is essential due to the limited GPU memory.

If you want to try the open-vocabulary segmentation, extract the CLIP features first:

python get_clip_features.py --image_root <path to the scene data>

Train 3D Gaussian Affinity Features

python train_contrastive_feature.py -m <path to the pre-trained 3DGS model> --iterations 10000 --num_sampled_rays 1000

3D Segmentation

Currently SAGA provides an interactive GUI (saga_gui.py) implemented with dearpygui and a jupyter-notebook (prompt_segmenting.ipynb). To run the GUI:

python saga_gui.py --model_path <path to the pre-trained 3DGS model>

Temporarily, open-vocabulary segmentation is only implemented in the jupyter notebook. Please refer to prompt_segmenting.ipynb for detailed instructions.

GUI Usage:

After set up the GUI, you can see the following interface:

Motion Control:

• left drag: Rotate.
• mid drag: Pan.
• right click: Input point prompt(s) (need to check the segmentation mode first).

Segmentation Control:

Hyper-parameter option:

• scale: The 3D scale (used for both segmentation and clustering).
• score thresh: The segmentation similarity threshold (used for segmentation).

Render option:

• RGB: Show the original RGB of current 3D-GS model at the specific viewpoint.
• PCA: Show the PCA decomposition results of 3D features of current 3D-GS model at the specific viewpoint.
• SIMILARITY: Show the similarity map of given point prompts (need to input prompts first).
• 3D CLUSTER: Show the 3D clustering results of current 3D-GS model.

Segmentation Mode option:

• click mode: Only one point can be input in this mode.
• multi-click mode: Multiple points can be input in this mode to select many objects simultaneously.
• preview_segmentation_in_2d: Show the 2D segmentation results with current input prompts (points, scale and score thresh). Note that the 2D segmentation results may be inconsistent with the 3D results.

Segmentation option:

After selecting the interest target(s). You can click segment3D to get the 3D segmentation results. If the results is not satisfied, you can click roll back to undo this segmentation or click clear to roll back to the unsegmented status, or you can continue to input prompts to conduct segmentation based on the temporary segmentation result. You can click save as to save the current segmentation results in ./segmentation_res/your_name.pt, which is a binary mask for all 3D Gaussians in the 3D-GS model.

Clustering option:

At any time, you can click cluster3d to get the clustering results of the current 3D-GS model. For example, you can directly cluster across the whole scene or cluster in the temporarily segmented objects for decomposition. Click reshuffle_cluster_color to shuffle the rendering colors of the clusters.

Note that directly clustering the whole scene may take a while, since we use HDBSCAN without the GPU support for convenience.

Rendering

After saving segmentation results in the interactive GUI or running the scripts in prompt_segmenting.ipynb, the bitmap of the Gaussians will be saved in ./segmentation_res/your_name.pt (you can set the name by yourself). To render the segmentation results on training views (get the segmented object by removing the background), run the following command:

python render.py -m <path to the pre-trained 3DGS model> --precomputed_mask <path to the segmentation results> --target scene --segment

To get the 2D rendered masks, run the following command:

python render.py -m <path to the pre-trained 3DGS model> --precomputed_mask <path to the segmentation results> --target seg

You can also render the pre-trained 3DGS model without segmentation:

python render.py -m <path to the pre-trained 3DGS model> --target scene

Citation

If you find this project helpful for your research, please consider citing the report and giving a ⭐.

@article{cen2023saga,
      title={Segment Any 3D Gaussians}, 
      author={Jiazhong Cen and Jiemin Fang and Chen Yang and Lingxi Xie and Xiaopeng Zhang and Wei Shen and Qi Tian},
      year={2023},
      journal={arXiv preprint arXiv:2312.00860},
}

Acknowledgement

The implementation of saga refers to GARField, OmniSeg3D, Gaussian Splatting, and we sincerely thank them for their contributions to the community.