Segment Anything in 3D with NeRFs
Jiazhong Cen1*, Zanwei Zhou1*, Jiemin Fang2,3†, Chen Yang1, Wei Shen1✉, Lingxi Xie2, Dongsheng Jiang2, Xiaopeng Zhang2, Qi Tian2
1AI Institute, SJTU 2Huawei Inc 3School of EIC, HUST .
*denotes equal contribution
†denotes project lead.
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.
- 2024/04/16: We release the 3D-GS version of SA3D (here). Now 3D segmentation can be achieved within seconds!
- 2023/11/11: We release the nerfstudio version of SA3D (here)! Currently it only supports the text prompt as input.
- 2023/06/29: We now support MobileSAM as the segmentation network. Follow the installation instruction in MobileSAM, and then download mobile_sam.pt into folder
./dependencies/sam_ckpt. You can use--mobile_samto switch to MobileSAM.
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
conda activate sa3d
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
(click to expand)
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_promptswhen the target object is extremely irregular like LLFF scenes Fern and Trex; - Use
--seg_posesto 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
Pointsin the drop down; click the original image to add a point prompt, then SAM will produce candidate masks; clickClear Pointsto clear out the previous inputs;point_prompt.mp4
-
Text Prompt: select
Textin the drop down;input your text prompt and clickGenerateto get candidate masks; note that unreasonable text input may cause error.text_prompt.mp4
-
-
Select your target mask;
-
Press
Start Trainingto 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:
If you find this project helpful for your research, please consider citing the report and giving a ⭐.
@inproceedings{cen2023segment,
title={Segment Anything in 3D with NeRFs},
author={Jiazhong Cen and Zanwei Zhou and Jiemin Fang and Chen Yang and Wei Shen and Lingxi Xie and Dongsheng Jiang and Xiaopeng Zhang and Qi Tian},
booktitle = {NeurIPS},
year = {2023},
}
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