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NOTE: The code has been further updated and optimized. Support for lower end GPUs has been added at the cost of accuracy. Please refer to changelog.md file.

Demonstration on the GARDEN scene. This shows the demonstration of positive and negative strokes.

Demonstration on the KITCHEN scene. This shows the demonstration of multiple positive strokes from different views.

Please note that there are two branches in this repository. The main branch contains the implementation of bilateral search which uses the spatio-semantic distance. The branch additional_spaces is an experimental branch which also accounts for the color latent vector of TensoRF for the calculation of search-distance.

If you want 2D masks or 3D masks (stored as checkpoints), you can checkout the data we have released. All the masks are created using our method.

To run the interactive segmentation, we require a GPU with 22-23GB of VRAM, we have tested the code on two consumer level GPUs RTX 3090 and RTX 4090.

The current configuration has been tested on Ubuntu 22.04. With CUDA 11.8.

conda create -n isrf python=3.9
conda activate isrf
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install torch-scatter -f https://data.pyg.org/whl/torch-1.13.1+cu116.html
pip install -r requirements.txt
conda install -c conda-forge faiss-gpu

Pytorch and torch_scatter installation is machine dependent, please install the correct version for your machine.

Follow the below directory structure for placing the data:

data
├── 360_v2             # Link: MIPNeRF360
│   └── [bicycle|bonsai|counter|garden|kitchen|room|stump]
│       ├── poses_bounds.npy
│       └── [images_2|images_4|images_8]
│
├── nerf_llff_data     # Link: NeRF-LLFF, Other LLFF
│   └── [fern|flower|fortress|horns|leaves|orchids|room|trex|chesstable|...]
│       ├── poses_bounds.npy
│       └── [images_2|images_4|images_8]

The code for feature extraction has been taken from N3F. Thanks to the original authors for providing it. Please follow the following instructions to prepare the features:

• To download the DINO checkpoint, run the following command:

  Ubuntu

  cd feature_extractor
  bash download_dino.sh
  

• To extract the DINO features and place them in the correct directory, run the following command. Note that we use the images downscaled by a factor of 8.

  python extract.py --dir_images ../data/nerf_llff_data/horns/images_4
  

To train the radiance field, run the following commands: The first Run is optimize for Radiance Fields, followed by distilling the DINO semantics into the Lattice.

cd ..
python run.py --config configs/llff/horns.py --stop_at 20000
python run.py --config configs/llff/horns.py --distill_active --weighted_distill_loss --stop_at 25000

Once the feature field is trained, the GUI can be launched using:

python gui.py --config configs/llff/horns.py

If the user wants to change the rendered resolution from given [1,2,4,8], resolution, he can do so by altering the config. file configs/llff/horns.py.

If you find our code, data or ideas useful, please cite our work with:

@inproceedings{isrfgoel2023,
    title={{Interactive Segmentation of Radiance Fields}}, 
    author={Goel, Rahul and Sirikonda, Dhawal and Saini, Saurabh and Narayanan, P.J.},
    year={2023},
    booktitle = {{Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}},
}