This is a repo for paper "Chat-3D: Data-efficiently Tuning Large Language Model for Universal Dialogue of 3D Scenes". [paper], [project page]

[2023.12.15] 🔥 We release a new version: Chat-3D v2 [code, paper], achieving strong performance on various 3D scene-language tasks.

• Prepare the environment:

  conda create -n chat-3d python=3.9.17
  conda activate chat-3d
  conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
  pip install -r requirements.txt

• Download LLaMA model:

  • Currently, we choose Vicuna-7B as the LLM in our model, which is finetuned from LLaMA-7B.
  • Download LLaMA-7B from Hugging Face.
  • Download vicuna-7b-delta-v0 and process it: (apply_delta.py is from Hugging Face)

  python3 model/apply_delta.py \
          --base /path/to/model_weights/llama-7b \
          --target vicuna-7b-v0 \
          --delta lmsys/vicuna-7b-delta-v0

  • Change the llama_model_path in config.py to the location of vicuna-7b-v0.

• Annotations and extracted features:

  For simplicity, we have made all the annotations available in annotations dir and extracted features available on Google Drive. Here are some brief explanations of the preparation:

  • Based on the annotations from ScanNet , we extract attributes (location, size, color) of objects from different scenes.

  • We use ULIP-2 to extract features of 3D objects.

  • The captions utilized in stage 1 and stage 2 are obtained from the annotations of ScanRefer.

• Object-centric dataset

  • We release the object-centric dataset in annotations dir, including train/val sets for conversation/detail instructions.

• Training (Instruction Tuning)

  Simply run the following scripts to sequentially tune from Stage 1 to Stage 3.

  # Stage 1
  ./scripts/run.sh --stage 1 \
                   --lr 5e-3
  
  # Stage 2
  ./scripts/run.sh --stage 2 \
                   --pretrained_path /path/to/pretrained_stage1.pth \
                   --lr 5e-3
  
  # Stage 3
  ./scripts/run.sh --stage 3 \
                   --pretrained_path /path/to/pretrained_stage2.pth \
                   --lr 5e-5

  We train the model on 4 A40 GPUs with 48GB VRAM. Here are some information about GPU usage and training time. (Note that we only use ScanRefer data for training currently, it would cost more training time if we add more training data in the future.)

  Stage	Batch Size	GPU Num	VRAM Usage per GPU	Training Time
  1	12	4	~ 25 GB	~ 5 min
  2	12	4	~ 45 GB	~ 1 hour
  3	1	4	~ 25 GB	~ 1.5 hour

• Inference

  Use one GPU for inference (set NUM_GPUS=1 in run.sh).

  ./scripts/run.sh --stage 3 \
                   --pretrained_path /path/to/pretrained_stage3.pth \
                   --evaluate

If you find this project useful in your research, please consider cite:

@article{wang2023chat,
  title={Chat-3d: Data-efficiently tuning large language model for universal dialogue of 3d scenes},
  author={Wang, Zehan and Huang, Haifeng and Zhao, Yang and Zhang, Ziang and Zhao, Zhou},
  journal={arXiv preprint arXiv:2308.08769},
  year={2023}
}
@article{huang2023chat,
  title={Chat-3D v2: Bridging 3D Scene and Large Language Models with Object Identifiers},
  author={Huang, Haifeng and Wang, Zehan and Huang, Rongjie and Liu, Luping and Cheng, Xize and Zhao, Yang and Jin, Tao and Zhao, Zhou},
  journal={arXiv preprint arXiv:2312.08168},
  year={2023}
}

Stay tuned for our project. 🔥

If you have any questions or suggestions, feel free to drop us an email ([email protected], [email protected]) or open an issue.

Thanks to the open source of the following projects:

VideoChat, LLaMA, ULIP, ScanRefer, ReferIt3D, vil3dref, ScanNet