Awesome

Revisit Anything: Visual Place Recognition via Image Segment Retrieval (ECCV 2024)

Dataset

Dataset Download

• To get the pipeline running quickly with a small dataset like 17places, you can download the required data from this box link. More details on this in Dataset preparation section.
• For datasets common with AnyLoc (i.e. Baidu, VPAir, pitts, 17places), you can download dataset from this for now.
• For SF-XL, MSLS and AmsterTime, find instructions here.

To get going quickly, it is suggested you start with a small dataset like 17places.

Dataset preparation

Say your main directory where you would be placing all your datasets is workdir_data. You may need to rename each of dataset folder/subfolder names as per the dataset name in place_rec_global_config.py. It must look like:

workdir_data/
├── baidu 
│   ├── training_images_undistort
│   ├── query_images_undistort
|   ├── out 
├── 17places 
│   ├── ref
│   ├── query 
|   ├── out 
|
and so on

Notes:

1. out folder is where all the pre-processed data will be saved and will be created by following scripts. You just need to ensure other 2 are present beforehand, which stand for reference database and query images.

Then, you can run the following commands to get the SegVLAD results for the datasets you want. (Also, you can replicate AnyLoc's results: See the config file.)

Download (preprocessed) data for the steps below here and place them at the appropriate location:

• Place models folder from sam_dino_models.zip inside workdir_data folder

To get the pipeline running quickly with a small dataset like 17places, you can download the required data from this box link and use it in following ways:

• 17places_only_dataset.zip (61 MB): You can download this and run 3 steps (+1 optional step below).
• 17places_full.zip (10.3 GB): If you download this, you can skip first 3 steps of below "segVLAD pipeline" and quickly run the core pipeline and replicate the results for this dataset.
• Ensure you place it appropriately as explained at the beginning of this subsection.

Running segVLAD pipeline

Env Setup

We tested the environment with Python 3.8 and torch 1.11.0

conda env create -f segvlad.yaml
conda activate segvlad
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
cd sam
pip install -e .

For running the finetuned model you will need the following additional dependencies.

pip install pytorch_lightning==1.9.0
pip install pytorch_metric_learning

Note:

• Please make sure to download the packages in this order since pytorch_lightning can break the env. For additional info about which version of pytorch_lightnitng to use with torch refer to this link.

Running the code

You can get the segVLAD pipeline up and running in few easy and quick steps as follows.

First, set the path where you stored datasets i.e. workdir_data in place_rec_global_config.py. Also see which experiment/dataset you want to run in place_rec_global_config.py. The default config we use in paper is exp0_global_SegLoc_VLAD_PCA_o3, i.e. nbr aggregation for order 3 with PCA. You can try out other orders with/without PCA if you'd like.

Then run the following scripts sequentially with one or both of these arguments. One example is: python place_rec_main.py --dataset 17places --experiment exp0_global_SegLoc_VLAD_PCA_o3 --vocab-vlad domain.

For full pipeline, you need to run first 3 scripts below for pre-processing (1. DINO/SAM extraction 2. (optional) generate VLAD cluster centers 3. Save pca model) and then the final 4th script to run the main segVLAD pipeline get the final results. (You can skip the first 3 steps in the case of 17places dataset by downloading preprocessed data as explained at the end of Dataset preparation section.)

1. For DINO/SAM extraction: (choose one of DINO or SAM)

   python place_rec_SAM_DINO.py --dataset <> --method DINO/SAM
   

   depending on which dataset and model you want to extract.
2. (Optional) For generating VLAD cluster center given a dataset (or its DINO desc path, to be precise):

   python vlad_c_centers_pt_gen.py --dataset <>
   

   NOTE: You don't need to run this step on your end, they already exist in cache folder. You can run this if you want to generate your own cluster centers say on a new dataset.
3. PCA extraction after the above are done: (Choose one of domain or map, you can start out with domain first)

   python place_rec_pca.py --dataset <> --experiment <> --vocab-vlad <domain/map>
   

4. Main SegVLAD pipeline after all the above are done:

   python place_rec_main.py --dataset <> --experiment <> --vocab-vlad <domain/map> --save-results <True/False>
   

   If you want to save the descriptors (for offline recall calculation later on), you can set save_results to True and results will automatically saved as experiment_name_date_time inside {workdir}/results/global/.

Additionally:

• The above scripts extract results for SegVLAD-PreT or pretrained case. If you want to run SegVLAD-FineT or finetuned experiments, just append the above scripts with _finetuned at the end, with below exception:
  • For step 1, you need to run place_rec_DINO_finetuned.py instead of place_rec_SAM_DINO.py and don't need to specify method argument. As you had already extracted SAM before, you just need finetuned DINO extraction here, so you can run:

  python place_rec_DINO_finetuned.py --dataset <>
  

Acknowledgements

We borrow some of the code from AnyLoc. We thank authors of AnyLoc for making their code public.