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InstanceRefer

InstanceRefer: Cooperative Holistic Understanding for Visual Grounding on Point Clouds through Instance Multi-level Contextual Referring

This repository is for the ICCV 2021 paper and 1st method on ScanRefer benchmark [paper].

Zhihao Yuan, Xu Yan, Yinghong Liao, Ruimao Zhang, Zhen Li*, Shuguang Cui

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

@InProceedings{yuan2021instancerefer,
  title={Instancerefer: Cooperative holistic understanding for visual grounding on point clouds through instance multi-level contextual referring},
  author={Yuan, Zhihao and Yan, Xu and Liao, Yinghong and Zhang, Ruimao and Li, Zhen and Cui, Shuguang},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={1791-1800},
  year={2021}
}

News

• 2021-07-23 InstanceRefer is accepted at ICCV 2021 :fire:!
• 2021-04-22 We release evaluation codes and pre-trained model!
• 2021-03-31 We release InstanceRefer v1 :rocket:!
• 2021-03-18 We achieve 1st place in ScanRefer leaderboard :fire:.

Getting Started

Setup

The code is tested on Ubuntu 16.04 LTS & 18.04 LTS with PyTorch 1.6 CUDA 10.1 and PyTorch 1.8 CUDA 10.2 installed.

conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -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 torchsaprse 1.2:

pip install --upgrade git+https://github.com/mit-han-lab/torchsparse.git
sudo apt-get install libsparsehash-dev

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

Data preparation

1. Download the ScanRefer dataset and unzip it under data/.
2. Downloadand the preprocessed GLoVE embeddings (~990MB) and put them under data/.
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
4. Used official and pre-trained PointGroup generate panoptic segmentation in PointGroupInst/. We provide pre-processed data in Baidu Netdisk [password: 0nxc].
5. Pre-processed instance labels, and new data should be generated in data/scannet/pointgroup_data/

cd data/scannet/
python prepare_data.py --split train --pointgroupinst_path [YOUR_PATH]
python prepare_data.py --split val   --pointgroupinst_path [YOUR_PATH]
python prepare_data.py --split test  --pointgroupinst_path [YOUR_PATH]

Finally, the dataset folder should be organized as follows.

InstanceRefer
├── data
│   ├── glove.p
│   ├── ScanRefer_filtered.json
│   ├── ...
│   ├── scannet
│   │  ├── meta_data
│   │  ├── pointgroup_data
│   │  │  ├── scene0000_00_aligned_bbox.npy
│   │  │  ├── scene0000_00_aligned_vert.npy
│   │  ├──├──  ... ...

Training

Train the InstanceRefer model. You can change hyper-parameters in config/InstanceRefer.yaml:

python scripts/train.py --log_dir instancerefer

Evaluation

You need specific the use_checkpoint with the folder that contains model.pth in config/InstanceRefer.yaml and run with:

python scripts/eval.py

Pre-trained Models

TODO

• Updating to the best version.
• Release codes for prediction on benchmark.
• Release pre-trained model.
• Merge PointGroup in an end-to-end manner.

Acknowledgement

This project is not possible without multiple great opensourced codebases.

• ScanRefer
• PointGroup
• torchsaprse
• pytorch_geometric

License

This repository is released under MIT License (see LICENSE file for details).