A Deep Feature Aggregation Network for Accurate Indoor Camera Localization

This is the PyTorch implementation of our paper "A Deep Feature Aggregation Network for Accurate Indoor Camera Localization".

Installation

To run our model, set up python3 environment from requirement.txt::

pip3 install -r requirement.txt

To compute pose by RANSAC-based PnP algorithm, you will need to build the cython module:

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

Datasets

7-Scenes: Download the dataset from the website.
12-Scenes: Download the dataset from the website.

Training and evaluating

Training on 7-Scenes dataset:

CUDA_VISIBLE_DEVICES=gpu_id python main.py --model fdanet --dataset 7S --scene chess --data_path ./data/ --flag train

training on 12-Scenes dataset:

CUDA_VISIBLE_DEVICES=gpu_id python main.py --model fdanet --dataset 12S --scene office2/5b --data_path ./data/ --flag train

evaluating on 7-Scenes dataset:

CUDA_VISIBLE_DEVICES=gpu_id python main.py --model fdanet --dataset 7S --scene chess --data_path ./data/ --flag test --resume model_path

evaluating on 12-Scenes dataset:


CUDA_VISIBLE_DEVICES=gpu_id python main.py --model fdanet --dataset 12S --scene office2/5b --data_path ./data/ --flag test --resume model_path

Examples:

CUDA_VISIBLE_DEVICES=1 python main.py --model fdanet --dataset 7S --scene chess --data_path ./data/ --flag test --resume /mnt/sda2/xt/7scenes/lap/chess_supervise_PFFM.pth

Pretrained Model.

We provide our pre-trained models on 7-Scenes link and on 12-Scenes link. Download them and move them to their according folders. For example, move the chess_supervise_PFFM.pth to train_log/7-Scenes/. Then revise --resume train_log/7-Scenes/chess_supervise_PFFM.pth.