Segment Anything in 3D with NeRFs
Jiazhong Cen*¹, Zanwei Zhou*¹, Jiemin Fang², Chen Yang¹, Wei Shen^1✉, Lingxi Xie³, Dongsheng Jiang³, Xiaopeng Zhang³, Qi Tian^3
1AI Institute, SJTU   ²School of EIC, HUST   ³Huawei Inc.
*denotes equal contribution

Given a NeRF, just input prompts from one single view and then get your 3D model.

We propose a novel framework to Segment Anything in 3D, named SA3D. Given a neural radiance field (NeRF) model, SA3D allows users to obtain the 3D segmentation result of any target object via only one-shot manual prompting in a single rendered view. The entire process for obtaining the target 3D model can be completed in approximately 2 minutes, yet without any engineering optimization. Our experiments demonstrate the effectiveness of SA3D in different scenes, highlighting the potential of SAM in 3D scene perception.

With input prompts, SAM cuts out the target object from the according view. The obtained 2D segmentation mask is projected onto 3D mask grids via density-guided inverse rendering. 2D masks from other views are then rendered, which are mostly uncompleted but used as cross-view self-prompts to be fed into SAM again. Complete masks can be obtained and projected onto mask grids. This procedure is executed via an iterative manner while accurate 3D masks can be finally learned. SA3D can adapt to various radiance fields effectively without any additional redesigning.

git clone https://github.com/Jumpat/SegmentAnythingin3D.git
cd SegmentAnythingin3D

conda create -n sa3d python=3.10
pip install -r requirements.txt

# Installing SAM
mkdir dependencies; cd dependencies 
mkdir sam_ckpt; cd sam_ckpt
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth
git clone [email protected]:facebookresearch/segment-anything.git 
cd segment-anything; pip install -e .

# Installing Grounding-DINO
git clone https://github.com/IDEA-Research/GroundingDINO.git
cd GroundingDINO/; pip install -e .
mkdir weights; cd weights
wget https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth

We now release the configs on these datasets:

• Foward-facing: LLFF
• Inward-facing: mip-NeRF360, LERF

data
├── 360_v2             # Link: https://jonbarron.info/mipnerf360/
│   └── [bicycle|bonsai|counter|garden|kitchen|room|stump]
│       ├── poses_bounds.npy
│       └── [images|images_2|images_4|images_8]
│
├── nerf_llff_data     # Link: https://drive.google.com/drive/folders/14boI-o5hGO9srnWaaogTU5_ji7wkX2S7
│   └── [fern|flower|fortress|horns|leaves|orchids|room|trex]
│       ├── poses_bounds.npy
│       └── [images|images_2|images_4|images_8]
│
└── lerf_data               # Link: https://drive.google.com/drive/folders/1vh0mSl7v29yaGsxleadcj-LCZOE_WEWB
    └── [book_store|bouquet|donuts|...]
        ├── transforms.json
        └── [images|images_2|images_4|images_8]

• Train NeRF

  python run.py --config=configs/llff/fern.py --stop_at=20000 --render_video --i_weights=10000

• Run SA3D in GUI

  python run_seg_gui.py --config=configs/llff/seg/seg_fern.py --segment \
  --sp_name=_gui --num_prompts=20 \
  --render_opt=train --save_ckpt

• Render and Save Fly-through Videos

  python run_seg_gui.py --config=configs/llff/seg/seg_fern.py --segment \
  --sp_name=_gui --num_prompts=20 \
  --render_only --render_opt=video --dump_images \
  --seg_type seg_img seg_density

Some tips when run SA3D:

• Increase --num_prompts when the target object is extremely irregular like LLFF scenes Fern and Trex;
• Use --seg_poses to specify the camera pose sequence used for training 3D mask, default='train', choices=['train', 'video'].

Using our Dash based GUI:

• Select which type of prompt to be used, currently support: Point Prompt and Text Prompt;

  • Point Prompt: select Points in the drop down; click the original image to add a point prompt, then SAM will produce candidate masks; click Clear Points to clear out the previous inputs;

    point_prompt.mp4

  • Text Prompt: select Text in the drop down;input your text prompt and click Generate to get candidate masks; note that unreasonable text input may cause error.

    text_prompt.mp4

• Select your target mask;

• Press Start Training to run SA3D; we visualize rendered masks and SAM predictions produced by our cross-view self-prompting stategy;

  start_train.mp4

• Wait a few minutes to see the final rendering results.

  results.mp4

• Refine the GUI, e.g., start from any train view, add more training hyper-parameter options, etc.;
• Support the two-pass stage in GUI; currently it may have some bugs.

SA3D can handle various scenes for 3D segmentation. Find more demos in our project page.

Forward facing	360°	Multi-objects

Thanks for the following project for their valuable contributions:

• Segment Anything
• DVGO
• Grounding DINO

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

@article{cen2023segment,
      title={Segment Anything in 3D with NeRFs}, 
      author={Jiazhong Cen and Zanwei Zhou and Jiemin Fang and Wei Shen and Lingxi Xie and Xiaopeng Zhang and Qi Tian},
      journal={arXiv:2304.12308},
      year={2023}
}