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CricaVPR

This is the official repository for the CVPR 2024 paper "CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition".

Getting Started

This repo follows the framework of GSV-Cities for training, and the Visual Geo-localization Benchmark for evaluation. You can download the GSV-Cities datasets HERE, and refer to VPR-datasets-downloader to prepare test datasets.

The test dataset should be organized in a directory tree as such:

├── datasets_vg
    └── datasets
        └── pitts30k
            └── images
                ├── train
                │   ├── database
                │   └── queries
                ├── val
                │   ├── database
                │   └── queries
                └── test
                    ├── database
                    └── queries

Before training, you should download the pre-trained foundation model DINOv2(ViT-B/14) HERE.

Train

python3 train.py --eval_datasets_folder=/path/to/your/datasets_vg/datasets --eval_dataset_name=pitts30k --foundation_model_path=/path/to/pre-trained/dinov2_vitb14_pretrain.pth --epochs_num=10

Test

To evaluate the trained model:

python3 eval.py --eval_datasets_folder=/path/to/your/datasets_vg/datasets --eval_dataset_name=pitts30k --resume=/path/to/trained/model/CricaVPR.pth

To add PCA:

python3 eval.py --eval_datasets_folder=/path/to/your/datasets_vg/datasets --eval_dataset_name=pitts30k --resume=/path/to/trained/model/CricaVPR.pth --pca_dim=4096 --pca_dataset_folder=pitts30k/images/train

Trained Model

You can directly download the trained model HERE.

Related Work

Our another work (two-stage VPR based on DINOv2) SelaVPR achieved SOTA performance on several datasets. The code is released at HERE.

Acknowledgements

Parts of this repo are inspired by the following repositories:

GSV-Cities

Visual Geo-localization Benchmark

DINOv2

Citation

If you find this repo useful for your research, please consider leaving a star⭐️ and citing the paper

@inproceedings{lu2024cricavpr,
  title={CricaVPR: Cross-image Correlation-aware Representation Learning for Visual Place Recognition},
  author={Lu, Feng and Lan, Xiangyuan and Zhang, Lijun and Jiang, Dongmei and Wang, Yaowei and Yuan, Chun},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  month={June},
  year={2024}
}