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
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