Awesome

This is a PyTorch implementation of 3DRefTR proposed by our paper "A Unified Framework for 3D Point Cloud Visual Grounding".

0. Installation

• (1) Install environment with environment.yml file:

  conda env create -f environment.yml --name 3dreftr
  

  • or you can install manually:

    conda create -n 3dreftr python=3.7
    conda activate 3dreftr
    conda install pytorch==1.9.0 torchvision==0.10.0 cudatoolkit=11.1 -c pytorch -c nvidia
    pip install numpy ipython psutil traitlets transformers termcolor ipdb scipy tensorboardX h5py wandb plyfile tabulate
    

• (2) Install spacy for text parsing

  pip install spacy
  # 3.3.0
  pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.3.0/en_core_web_sm-3.3.0.tar.gz
  

• (3) Compile pointnet++

  cd ~/3DRefTR
  sh init.sh
  

• (4) Install segmentator from https://github.com/Karbo123/segmentator

1. Quick visualization demo

We showing visualization via wandb for superpoints, kps points, bad case analyse, predict/ground_truth masks and box.

• superpoints in 'src/joint_det_dataset.py' line 71

self.visualization_superpoint = False

• others in 'src/groungd_evaluation.py' line 66 ~ 70

self.visualization_pred = False
self.visualization_gt = False
self.bad_case_visualization = False
self.kps_points_visualization = False
self.bad_case_threshold = 0.15

2. Data preparation

The final required files are as follows:

├── [DATA_ROOT]
│	├── [1] train_v3scans.pkl # Packaged ScanNet training set
│	├── [2] val_v3scans.pkl   # Packaged ScanNet validation set
│	├── [3] ScanRefer/        # ScanRefer utterance data
│	│	│	├── ScanRefer_filtered_train.json
│	│	│	├── ScanRefer_filtered_val.json
│	│	│	└── ...
│	├── [4] ReferIt3D/        # NR3D/SR3D utterance data
│	│	│	├── nr3d.csv
│	│	│	├── sr3d.csv
│	│	│	└── ...
│	├── [5] group_free_pred_bboxes/  # detected boxes (optional)
│	├── [6] gf_detector_l6o256.pth   # pointnet++ checkpoint (optional)
│	├── [7] roberta-base/     # roberta pretrained language model
│	├── [8] checkpoints/      # 3dreftr pretrained models

• [1] [2] Prepare ScanNet Point Clouds Data
  • 1) Download ScanNet v2 data. Follow the ScanNet instructions to apply for dataset permission, and you will get the official download script download-scannet.py. Then use the following command to download the necessary files:

    python2 download-scannet.py -o [SCANNET_PATH] --type _vh_clean_2.ply
    python2 download-scannet.py -o [SCANNET_PATH] --type _vh_clean_2.labels.ply
    python2 download-scannet.py -o [SCANNET_PATH] --type .aggregation.json
    python2 download-scannet.py -o [SCANNET_PATH] --type _vh_clean_2.0.010000.segs.json
    python2 download-scannet.py -o [SCANNET_PATH] --type .txt
    

    where [SCANNET_PATH] is the output folder. The scannet dataset structure should look like below:

    ├── [SCANNET_PATH]
    │   ├── scans
    │   │   ├── scene0000_00
    │   │   │   ├── scene0000_00.txt
    │   │   │   ├── scene0000_00.aggregation.json
    │   │   │   ├── scene0000_00_vh_clean_2.ply
    │   │   │   ├── scene0000_00_vh_clean_2.labels.ply
    │   │   │   ├── scene0000_00_vh_clean_2.0.010000.segs.json
    │   │   ├── scene.......
    

  • 2) Package the above files into two .pkl files(train_v3scans.pkl and val_v3scans.pkl):

    python Pack_scan_files.py --scannet_data [SCANNET_PATH] --data_root [DATA_ROOT]
    

• [3] ScanRefer: Download ScanRefer annotations following the instructions HERE. Unzip inside [DATA_ROOT].
• [4] ReferIt3D: Download ReferIt3D annotations following the instructions HERE. Unzip inside [DATA_ROOT].
• [5] group_free_pred_bboxes: Download object detector's outputs. Unzip inside [DATA_ROOT]. (not used in single-stage method)
• [6] gf_detector_l6o256.pth: Download PointNet++ checkpoint into [DATA_ROOT].
• [7] roberta-base: Download the roberta pytorch model:

  cd [DATA_ROOT]
  git clone https://huggingface.co/roberta-base
  cd roberta-base
  rm -rf pytorch_model.bin
  wget https://huggingface.co/roberta-base/resolve/main/pytorch_model.bin
  

• [8] checkpoints: Our pre-trained models (see 3. Models).
• [9] ScanNetv2: Prepare the preporcessed ScanNetv2 dataset follow "Data Preparation" section from https://github.com/sunjiahao1999/SPFormer, obtaining the dataset file with the following structure:

ScanNetv2
├── data
│   ├── scannetv2
│   │   ├── scans
│   │   ├── scans_test
│   │   ├── train
│   │   ├── val
│   │   ├── test
│   │   ├── val_gt

• [10] superpoints: Prepare superpoints for each scene preprocessed from Step. 9.

  cd [DATA_ROOT]
  python superpoint_maker.py  # modify data_root & split
  

3. Models

4. Training

• Please specify the paths of --data_root, --log_dir, --pp_checkpoint in the train_*.sh script first.
• For ScanRefer training

  sh scripts/train_scanrefer_3dreftr_hr.sh
  sh scripts/train_scanrefer_3dreftr_sp.sh
  

• For ScanRefer (single stage) training

  sh scripts/train_scanrefer_3dreftr_hr_single.sh
  sh scripts/train_scanrefer_3dreftr_sp_single.sh
  

• For SR3D training

  sh scripts/train_sr3d_3dreftr_hr.sh
  sh scripts/train_sr3d_3dreftr_sp.sh
  

• For NR3D training

  sh scripts/train_nr3d_3dreftr_hr.sh
  sh scripts/train_nr3d_3dreftr_sp.sh
  

5. Evaluation

• Please specify the paths of --data_root, --log_dir, --checkpoint_path in the test_*.sh script first.
• For ScanRefer evaluation

  sh scripts/test_scanrefer_3dreftr_hr.sh
  sh scripts/test_scanrefer_3dreftr_sp.sh
  

• For ScanRefer (single stage) evaluation

  sh scripts/test_scanrefer_3dreftr_hr_single.sh
  sh scripts/test_scanrefer_3dreftr_sp_single.sh
  

• For SR3D evaluation

  sh scripts/test_sr3d_3dreftr_hr.sh
  sh scripts/test_sr3d_3dreftr_sp.sh
  

• For NR3D evaluation

  sh scripts/test_nr3d_3dreftr_hr.sh
  sh scripts/test_nr3d_3dreftr_sp.sh
  

6. Acknowledgements

This repository is built on reusing codes of EDA. We recommend using their code repository in your research and reading the related article. We are also quite grateful for SPFormer, BUTD-DETR, GroupFree, ScanRefer, and SceneGraphParser.

7. Citation

If you find our work useful in your research, please consider citing:

@misc{lin2023unified,
      title={A Unified Framework for 3D Point Cloud Visual Grounding}, 
      author={Haojia Lin and Yongdong Luo and Xiawu Zheng and Lijiang Li and Fei Chao and Taisong Jin and Donghao Luo and Chengjie Wang and Yan Wang and Liujuan Cao},
      year={2023},
      eprint={2308.11887},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}