This is the PyTorch implementation of our paper "EAAINet: An Element-wise Attention Network with Global Affinity Information for Accurate Indoor Visual Localization".

Installation

Firstly, we need to set up python3 environment from requirement.txt:

pip3 install -r requirement.txt 

Subsequently, we need to build the cython module to install the PnP solver:

cd ./pnpransac
rm -rf build
python setup.py build_ext --inplace

Datasets

We utilize two standard datasets (i.e, 7-Scenes and 12-Scenes) to evaluate our method.

• 7-Scenes: The 7-Scenes dataset can be downloaded from 7-Scenes.
• 12-Scenes: The 12-Scenes dataset can be downloaded from 12-Scenes.

Evaluation

The pre-trained models can be downloaded from 7-Scenes ansd 12-Scenes. Then, we can run tran_7S.sh or train_12S.sh to evaluate EAAINet on 7-Scenes and 12-Scenes datasets.

bash tran_7S.sh

Notably, we need to modify the path of the downloaded models in tran_7S.sh or train_12S.sh. The meaning of each part of tran_7S.sh or train_12S.sh is as follows:

python main.py --model multi_scale_trans -dataset [7S/12S] --scene [scene name, such as chess] 
               --data_path ./data/ --flag test --resume [model_path]

Training

We can run the tran_7S.sh or train_12S.sh to train EAAINet. The meaning of each part of tran_7S.sh or train_12S.sh is as follows:

python main.py --model multi_scale_trans -dataset [7S/12S] --scene [scene name, such as chess] --data_path ./data/ 
               --flag train --n_epoch 500 --savemodel_path [save_path]

Acknowledgements

The PnP-RANSAC pose solver is referenced from HSCNet. The sensor calibration file and the normalization translation files for the 7-Scenes dataset are from DSAC.