| # Gaussian Grouping: Segment and Edit Anything in 3D Scenes |
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| We provide dataset format and custom dataset preparation in the [training doc](./train.md). Here we introduce the LERF-Mask dataset proposed in our paper and its evaluation. |
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| ## 1. LERF-Mask dataset |
| You can download LERF-Mask dataset from [this hugging-face link](https://huggingface.co/mqye/Gaussian-Grouping/tree/main/data/lerf_mask). Test set of LERF-Mask dataset includes 2-4 novel view images. The mask annotations are saved in `test_mask` folder. The name of each mask image corresponds to the input text-prompt. |
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| ``` |
| lerf_mask |
| |____figurines |
| | |____distorted |
| | |____images |
| | |____images_train |
| | |____object_mask |
| | |____sparse |
| | |____stereo |
| | |____test_mask |
| | |____<novel view 0> |
| | | |____<text prompt 0>.png |
| | | |____... |
| | |____<novel view 1> |
| | | |____<text prompt 0>.png |
| | | |____... |
| |____ramen |
| | |____... |
| |____teatime |
| | |____... |
| ``` |
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| ## 2. Render mask with text-prompt |
| For semantic information of each mask output, since SAM masks are class-agnostic, we can use a vision-language detector's mask output, for example [grounded-sam](https://github.com/IDEA-Research/Grounded-Segment-Anything), to match our mask to give semantic information. |
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| We test our segmentation with a simple strategy using grounded-sam on the first frame for text-prompt. You can use the following command with the provided checkpoints on [hugging face](https://huggingface.co/mqye/Gaussian-Grouping/tree/main/checkpoint) or your own training result. In the future we can also explore better detectors and prompt formats. |
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| ``` |
| python render_lerf_mask.py -m output/lerf_pretrain/figurines --skip_train |
| python render_lerf_mask.py -m output/lerf_pretrain/ramen --skip_train |
| python render_lerf_mask.py -m output/lerf_pretrain/teatime --skip_train |
| ``` |
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| ## 3. LERF-Mask evaluation |
| We provide our result on [hugging face](https://huggingface.co/mqye/Gaussian-Grouping/tree/main/result). We also provide a script for evaluating IoU and Boundary-IoU. You can change the output path to your output folder and run the script. |
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| For example, |
| ``` |
| python script/eval_lerf_mask.py figurines |
| python script/eval_lerf_mask.py ramen |
| python script/eval_lerf_mask.py teatime |
| ``` |
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