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3D-SPS

Code for our CVPR 2022 Oral paper "3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection".

[arXiv] [BibTeX]

<div align="center"> <img src="docs/framework.png"/> </div><br/>

Dataset

If you would like to access to the ScanRefer dataset, please fill out this form. Once your request is accepted, you will receive an email with the download link.

Note: In addition to language annotations in ScanRefer dataset, you also need to access the original ScanNet dataset. Please refer to the ScanNet Instructions for more details.

Download the dataset by simply executing the wget command:

wget <download_link>

Data format

"scene_id": [ScanNet scene id, e.g. "scene0000_00"],
"object_id": [ScanNet object id (corresponds to "objectId" in ScanNet aggregation file), e.g. "34"],
"object_name": [ScanNet object name (corresponds to "label" in ScanNet aggregation file), e.g. "coffee_table"],
"ann_id": [description id, e.g. "1"],
"description": [...],
"token": [a list of tokens from the tokenized description]

Setup

The code is now compatiable with PyTorch 1.6! Please execute the following command to install PyTorch

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch

Install the necessary packages listed out in requirements.txt:

pip install -r requirements.txt

After all packages are properly installed, please run the following commands to compile the CUDA modules for the PointNet++ backbone:

cd lib/pointnet2
python setup.py install

Before moving on to the next step, please don't forget to set the project root path to the CONF.PATH.BASE in config/default.yaml.

Data preparation

  1. Download the ScanRefer dataset and unzip it under data/.

  2. Download the text embeddings:

  3. Download the ScanNetV2 dataset and put (or link) scans/ under (or to) data/scannet/scans/ (Please follow the ScanNet Instructions for downloading the ScanNet dataset).

After this step, there should be folders containing the ScanNet scene data under the data/scannet/scans/ with names like scene0000_00

  1. Pre-process ScanNet data. A folder named scannet_data/ will be generated under data/scannet/ after running the following command. Roughly 3.8GB free space is needed for this step:
cd data/scannet/
python batch_load_scannet_data.py

After this step, you can check if the processed scene data is valid by running:

python visualize.py --scene_id scene0000_00

  1. Download the pre-trained PointNet++ backbone (Google Drive or Baidu Drive(passcode: likl)])

  2. (Optional) Pre-process the multiview features from ENet.

    a. Download the ENet pretrained weights (1.4MB) and put it under data/

    b. Download and decompress the extracted ScanNet frames (~13GB).

    c. Change the data paths in config.py marked with TODO accordingly.

    d. Extract the ENet features:

    python script/compute_multiview_features.py

    e. Project ENet features from ScanNet frames to point clouds; you need ~36GB to store the generated HDF5 database:

    python script/project_multiview_features.py --maxpool

    You can check if the projections make sense by projecting the semantic labels from image to the target point cloud by:

    python script/project_multiview_labels.py --scene_id scene0000_00 --maxpool

Usage

Training

python scripts/train.py --config ./config/default.yaml

For more training options (like using preprocessed multiview features), please see details in default.yaml.

Evaluation

To evaluate the trained ScanRefer models, please download the trained model(Google Drive or Baidu Drive(passcode: x3vl)]) and put it in the <folder_name> under outputs/ and run :

python scripts/eval.py --config ./config/default.yaml --folder <folder_name> --reference --no_nms --force

Acknowledgement

We would like to thank the authors of ScanRefer and Group-Free for their open-source release.

License

3D-SPS is released under the MIT license.

<a name="CitingSPS"></a>Citation

Consider cite 3D-SPS in your publications if it helps your research.

@article{luo20223d,
  title={3D-SPS: Single-Stage 3D Visual Grounding via Referred Point Progressive Selection},
  author={Luo, Junyu and Fu, Jiahui and Kong, Xianghao and Gao, Chen and Ren, Haibing and Shen, Hao and Xia, Huaxia and Liu, Si},
  journal={arXiv preprint arXiv:2204.06272},
  year={2022}
}