TSP3D: Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding

This repo contains the official PyTorch implementation for paper Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding.

Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding
Wenxuan Guo*, Xiuwei Xu*, Ziwei Wang, Jianjiang Feng$^\dagger$, Jie Zhou, Jiwen Lu

* Equal contribution $\dagger$ Corresponding author

In this work, we propose an efficient multi-level convolution architecture for 3D visual grounding. TSP3D achieves superior performance compared to previous approaches in both inference speed and accuracy.

News

• [2025/2/27]: TSP3D is accepted to CVPR25 with final reviews of all accepts (the highest ratings)!!
• [2025/2/16]: Code and arxiv paper released.

Method

Method Pipeline:

Getting Started

1. Installation

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

  conda env create -f environment.yml --name TSP3D
  

  • or you can install manually:

    conda create -n TSP3D python=3.9
    conda activate TSP3D
    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) Follow mmdetection3d to install mmcv, mmdet and mmdet3d.

• (4) Install MinkowskiEngine.

  conda install openblas-devel -c anaconda
  pip install -U git+/~https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option="--blas_include_dirs=/opt/conda/include" --install-option="--blas=openblas"

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] roberta-base/     # roberta pretrained language model
│	├── [6] checkpoints/      # We will release the trained 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] 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
  

3. Training

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

  sh scripts/train_scanrefer_single.sh
  

• For SR3D training

  sh scripts/train_sr3d.sh
  

• For NR3D training

  sh scripts/train_nr3d.sh
  

4. Evaluation

• Please specify the paths of --data_root, --log_dir, --checkpoint_path in the test_*.sh script first. When evaluating inference speed, please use a batch size of 1 and a single GPU.
• For ScanRefer evaluation

  sh scripts/test_scanrefer_single.sh
  

• For ScanRefer evaluation

  sh scripts/test_scanrefer_single.sh
  

• For SR3D evaluation

  sh scripts/test_sr3d.sh
  

• For NR3D evaluation

  sh scripts/test_nr3d.sh
  

Main Results

• We provide the checkpoints for quick reproduction of the results reported in the paper.

  Benchmark	Pipeline	Acc@0.25	Acc@0.5	Inference Speed (FPS)	Downloads
  ScanRefer	Single-stage	56.45	46.71	12.43	model

  Benchmark	Pipeline	Acc@0.25	Acc@0.5	Downloads
  Nr3d	Single-stage	48.7	37.0	model
  Sr3d	Single-stage	57.1	44.1	model

• Comparison of 3DVG methods on ScanRefer dataset:

Visualization

• Visualization of the text-guided pruning process:

• Visualization of the completion-based addition process:

Acknowledgements

We are quite grateful for DSPDet3D, BUTD-DETR, EDA, ScanRefer and TR3D.

Citation

@article{guo2025tsp3d, 
      title={Text-guided Sparse Voxel Pruning for Efficient 3D Visual Grounding}, 
      author={Wenxuan Guo and Xiuwei Xu and Ziwei Wang and Jianjiang Feng and Jie Zhou and Jiwen Lu},
      journal={arXiv preprint arXiv:2502.10392},
      year={2025}
}