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<br /> <p align="center"> <h1 align="center">3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding </h1> <p align="center"> <a href="https://staymylove.github.io">Zeju Li</a>, Chao Zhang, Xiaoyan Wang, Ruilong Ren, Yifan Xu, Ruifei Ma, Xiangde Liu </p> <p align="center"> <a href='https://arxiv.org/abs/2401.03201'> <img src='https://img.shields.io/badge/Paper-PDF-red?style=flat&logo=arXiv&logoColor=red' alt='Paper PDF'> </a> </p> <p align="center"> <img src="figs/overview.png" alt="Logo" width="80%"> </p> </p>

Description

Official implementation of the paper: 3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding. This paper is accepted by ICME-3DMM.

Setup

To set up the environment, run the following commands:

conda create -n 3dmit python==3.10.13
conda activate 3dmit

pip install torch==1.13.1+cu117 torchvision==0.14.1+cu117 -f https://download.pytorch.org/whl/torch_stable.html
pip install -r requirements.txt

Data

Training data

For source 3D scene point clouds,

  1. scannet

    you can download the processed 3D pcl files by this link:

    https://drive.google.com/file/d/1vTcOFmTK0jvbRpPqggWj2cWx4gA7ulrE/view?usp=sharing

  2. 3rscan

    you can download the 3D pcl files by its website:

    https://github.com/WaldJohannaU/3RScan

For language instructions, there are tasks including VQA, VG, multi-choice, detection, conversations, etc.

You can download them by this link:

https://drive.google.com/file/d/1s1ehz8Q6WX9bCghVNtL9sPeulb6886I0/view?usp=sharing

put them in this format:

./datasets/data/3D_Instruct/meta_file
    ├── VQA_all_84w.json  # there are all the data, 840K from 3rscan and scannet
    ├── VQA_all_75w.json  # only from scannet, 740K 3d-text pairs
    ├── VQA_3rscan.json   # 9572 3d-text pairs from 3rscan

Eval data

For language instructions, there are 4 eval tasks, you can find them in

./datasets/data/3D_Benchmark/meta_file/
    ├── VQA
    ├── multi-choice
    ├── visual grounding
          ├── obj location prediction
          ├── obj index prediction
    ├── detection

3D features

For scannet:

scannet_attributes.json      
scannet_uni3d_feats_1024.pt
scannet_train_attributes.pt  
scannet_uni3d_feats.pt
scannet_ulip2_feats.pt

For 3rscan:

3rscan_attributes.json     
3rscan_ulip2_feats.pt       
3rscan_uni3d_feats_1024.pt

download link:

https://drive.google.com/file/d/12kXvxn9iYI20l-5k6MEpyONr1sEGr2o2/view?usp=sharing

Model_zoo

src/
├── model_zoo/
│   ├── epcl_ckpts/
│          ├── epcl_scannet_vit-L-14_256tokens_latest.pth 
│   ├── vicuna-7b-v0
│   ├── vicuna-13b-v0
│   ├── llava1.6-7b
│   └── llava1.6-13b

You can download the epcl checkpoint by this link:

https://drive.google.com/file/d/177yY53BGMELlVFWlmHYArE0HlCFsCntW/view?usp=sharing

Ckpt & result

For the result of 3DMIT(Vicuna-7b) with Ulip2 :

https://drive.google.com/file/d/1Debdd_ZsjiAPhlmrnSgjotPO5XMBcm1T/view?usp=sharing

For the result of 3DMIT(Vicuna-7b) with Uni3D :

https://drive.google.com/file/d/1qTrEtpfG2L-luOcfX7L9JySHOeDjoAtr/view?usp=sharing

Model

only using scene info

./src/model/3dmit-onlyscene-512.py

using scene + objects info

./src/model/3dmit.py

using scene + objects + 2D imgs info

./src/model/3dmit-scene+obj+img-512.py

Run

Train 3DMIT

bash ./src/scripts/3DMIT_training.sh

Eval 3DMIT for VQA/description/caption tasks

bash ./src/scripts/3DMIT_3D_Evaluation_7b.sh

for visual grounding task

python ./src/vg_eval_script.py

Citation

If you find our work useful, please consider citing:

@article{li20243dmit,
  title={3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding},
  author={Li, Zeju and Zhang, Chao and Wang, Xiaoyan and Ren, Ruilong and Xu, Yifan and Ma, Ruifei and Liu, Xiangde},
  journal={arXiv preprint arXiv:2401.03201},
  year={2024}
}

Acknowledge

Our based code:

https://github.com/OpenGVLab/LAMM

https://github.com/Chat-3D/Chat-3D-v2

https://github.com/Chat-3D/Chat-3D

https://github.com/baaivision/Uni3D