conda env create -f environment.yaml
conda activate vsnet
cd scripts
sh environment.sh

python scripts/7scenes_preprocess.py --scene scene_name --root_dir /path/to/data
python scripts/cambridge_preprocess.py --scene scene_name --root_dir /path/to/data

7scenes_release
├── chess
│   ├── id2centers.json
│   ├── TrainSplit.txt
│   ├── TestSplit.txt
│   ├── seq-01
│   │   ├── frame-000000.color.png
│   │   ├── frame-000000.depth.png
│   │   ├── frame-000000.pose.txt
│   │   ├── frame-000000.seg.png
│   │   ├── frame-000000.vertex_2d.npy
│   │   ├── ...
│   ├── seq-02
│   ├── ...
├── heads
├── ...

python eval.py --dataset {dataset}_loc --resume true --config /path/to/config.json
# Example
# python eval.py --dataset 7scenes_loc --resume true --config collected_configs/7scenes/r.json

python train.py --dataset {dataset}_loc --scene {scene_name} --use-aug true --gpu-id gpu_device_id
# Example
# python train.py --dataset 7scenes_loc --scene heads --use-aug true --gpu-id 1

@inproceedings{huang2021vs,
  title={{VS-Net}: Voting with Segmentation for Visual Localization},
  author={Huang, Zhaoyang and Zhou, Han and Li, Yijin and Yang, Bangbang and Xu, Yan and Zhou, Xiaowei and Bao, Hujun and Zhang, Guofeng and Li, Hongsheng},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6101--6111},
  year={2021}
}

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