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GroundedSAM for OccNeRF

In order to generate per-pixel semantic labels for OccNeRF without any 3D ground truth, we leverage Grounded-Segment-Anything and modify it to generate semantic labels from 2D image training data.

Updates

• 🔔 2023/12/14 Release the code as a data preparing tool for OccNeRF

Installation

The code requires python>=3.8, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.

Prepare environment:

git clone git@github.com:JunchengYan/GroundedSAM_OccNeRF.git
cd GroundedSAM_OccNeRF/
pip install -r requirements.txt

Install Segment Anything:

python -m pip install -e segment_anything

Install GroundingDINO:

python -m pip install -e GroundingDINO

Other dependency

pip install diffusers transformers accelerate scipy safetensors

Run the code

Step1: Download the pretrained weights

Prepare weight for Segment Anything:

# Place it under GroundedSAM_OccNeRF/
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth 

Step2: Prepare the models

Due to the unstable Internet connection, we do not follow the instruction of Grounded-Segment-Anything, which downloads the GroundiongDINO and BERT model using Hugging Face. We download the model from Hugging Face in advance and modified the code to load from local.

Load models from local

You can download the GroundiongDINO model from here, and BERT model from here. Then unzip the file under GroundedSAM_OccNeRF/

unzip models--ShilongLiu--GroundingDINO.zip
unzip models--bert-base-uncased.zip

Load models from Hugging Face

You can also change the function load_model_hf() in groundedsam_generate_sem_demo.py and groundedsam_generate_sem_nusc.py to the original code in Grounded-Segment-Anything to load GroundingDINO from Hugging Face. You should also change the function get_pretrained_language_model() in GroundingDINO/util/get_tokenlizer.py back to the code in Grounded-Segment-Anything to load BERT

Step3: Generate semantic labels

Generate semantic labels on Nuscenes trainset

Prepare data

If you are using GroundedSAM_OccNeRF individually, please link your nuscenes dataset path to the GroundedSAM_OccNeRF/ folder and download metadata according to OccNeRF, then modify the data_path in groundedsam_generate_sem_nusc.py.

ln -s DATA_PATH ./data

We use groundedsam_generate_sem_nusc.py to generate semantic labels for OccNeRF self-supervised occupancy learning. Modify sava_path in groundedsam_generate_sem_nusc.py to determine where to save the results. We need about 36 hours to generate semantic masks for Nuscenes trainset on 8 RTX3090 GPUs.

bash run.sh

Generate semantic labels on any data

We use groundedsam_generate_sem_demo.py to generate semantic masks of any picture on a single GPU. You can modify data_path and sava_path in groundedsam_generate_sem_demo.py according to your need. The pictures under bikes/ can be used for test.

bash infer.sh

Bibtex

If this work is helpful for your research, please consider citing the following BibTeX entry.

@article{chubin2023occnerf, 
      title   = {OccNeRF: Self-Supervised Multi-Camera Occupancy Prediction with Neural Radiance Fields}, 
      author  = {Chubin Zhang and Juncheng Yan and Yi Wei and Jiaxin Li and Li Liu and Yansong Tang and Yueqi Duan and Jiwen Lu},
      journal = {arXiv preprint arXiv:TODO},
      year    = {2023}
}