The implementation of the paper "A Deep Feature Aggregation Network for Accurate Indoor Camera Localization".

Related tags

Deep LearningFDANET
Overview

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
Owner
GitHub Repository
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