Code for "Are Local Features All You Need for Cross-Domain Visual Place Recognition?" CVPR IMW 2023. The code will be released before the 18th of June 2023 (the conference date).
You can download and format the datasets following the instrunctions here . Notice that many datasets are released only under request, SX-XL test database and queries (included our sf-xl night and occlusion) can be requested at this link.
The final dataset folder structure and filename format should be:
datasets --- database --- [subfolders] --- @UTM_east@UTM_north...jpg
@UTM_east@UTM_north...jpg
...
queries --- [subfolders] --- @UTM_east@UTM_north...jpg
@UTM_east@UTM_north...jpg
...
...
The required Python packages are specified in requirements.txt . To install all the third part code run the following commands:
mkdir third_party
cd third_party
git clone https://github.com/naver/r2d2.git
git clone https://github.com/mihaidusmanu/d2-net.git
git clone https://github.com/magicleap/SuperGluePretrainedNetwork
git clone https://github.com/sungonce/CVNet.git
git clone https://github.com/QVPR/Patch-NetVLAD
git clone https://github.com/RuotongWANG/TransVPR-model-implementation
Download CVNet pretrained weights here and to save them inside third_party/CVNet. To download D2-Net pretrained weights follow the instructions here. Other models already download the weights or they will perform the dowload the first time they run.
To extract global embeddings and to perform global retrieval you can use scripts\extract_global_descriptors.py. The scripts takes the following main parameters:
- output_base_name: an identifier that will be added as prefix at every outputs file.
- output_folder: where to save the outputs.
- extractor: the global extractor class and parameters to be instantiated. The only option supported at the moment is CosPlace.
- n_chunks: default 1, can be usefull to increase it if the database embeddings do not fit the RAM.
- dataset_path: path to the folder that contains database and queries subfolders.
- database_folder: the database subfolder.
- queries_folder: the queries subfolder.
An example command usage is:
python scripts/extract_global_descriptors.py --output_base_name="cosplace_50_sf_xl_queries_v1" --extractor="CosPlace()" --n_chunks=4 --output_folder="outputs" --dataset_path="/data/sf_xl/processed/test/" --database_folder="database" --queries_folder="queries_v1"
You can find the outputs in the output_folder:
- <output_base_name>.h5: cotanins top_candidates_index [N_queries, N_candidates] dataset indeces of queries and predictions, top_candidates_distances [N_queries, N_candidates] retrieval score, top_candidates_filenames [N_queries, N_candidates] filenames of top predictions, queries_filenames [N_queries] filenames of queries.
- <output_base_name>_recalls.txt: recalls of the global retrieval step.
The script offers also the possibility to extract in parallel and evaluate the top predictions subsequently with --perform_evaluation and --chunks parameters as it is shown in the example:
export CUDA_VISIBLE_DEVICES=0
python scripts/extract_global_descriptors.py --output_base_name="cosplace_50_sf_xl_queries_v1" --extractor="CosPlace()" --n_chunks=2 --chunks=1 --perform_evaluation=0 --output_folder="outputs" --dataset_path="/data/sf_xl/processed/test/" --database_folder="database" --queries_folder="queries_v1" >chunk0.out 2>chunk0.err &
export CUDA_VISIBLE_DEVICES=1
python scripts/extract_global_descriptors.py --output_base_name="cosplace_50_sf_xl_queries_v1" --extractor="CosPlace()" --n_chunks=2 --chunks=1 --perform_evaluation=0 --output_folder="outputs" --dataset_path="/data/sf_xl/processed/test/" --database_folder="database" --queries_folder="queries_v1" >chunk1.out 2>chunk1.err &
# After extraction is performed
python scripts/extract_global_descriptors.py --output_base_name="cosplace_50_sf_xl_queries_v1" --extractor="CosPlace()" --n_chunks=2 --chunks=-1 --output_folder="outputs" --dataset_path="/data/sf_xl/processed/test/" --database_folder="database" --queries_folder="queries_v1"
To perform re-ranking run scripts/rerank.py. An example command is below, the file benchmark.sh provide all the command parameters to reproduce our experiments. It requires to specify which global retrivial top candidates presaved file to load with the parameter base_predictions_path.
python scripts/rerank.py --output_base_name="r2d2_sf_xl_queries_v1" --extractor="R2D2()" --scorer="RANSAC(ransacReprojThreshold=24)" --base_predictions_path="outputs/cosplace_50_sf_xl_queries_v1.h5" --output_folder="outputs" --dataset_path="/data/sf_xl/processed/test/" --database_folder="database" --queries_folder="queries_v1"
The outputs will be saved in the output_folder.
<output_base_name>_reranking.h5: predictions [N_queries, N_candidates] the re-ranked candidates, scores [N_queries, N_candidates] the re-ranking scores, reranked_candidates_filenames [N_queries, N_candidates] filenames of top predictions, queries_filenames [N_queries] filenames of queries. <output_base_name>_recalls.txt: recalls of the re-ranking step.
- Cospalce presaved top candidates and recalls: coming soon...
- Re-ranking presaved top candidates and recalls: coming soon...
This library make use of code taken from the following libraries:
Here is the bibtex to cite our paper
@InProceedings{Barbarani_2023_CVPR,
author = {Barbarani, Giovanni and Mostafa, Mohamad and Bayramov, Hajali and Trivigno, Gabriele and Berton, Gabriele and Masone, Carlo and Caputo, Barbara},
title = {Are Local Features All You Need for Cross-Domain Visual Place Recognition?},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2023},
}
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