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d2s

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[ICRA 2025 & RAL 2024] D2S: Representing sparse descriptors and 3D coordinates for camera relocalization

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# D2S: Representing sparse descriptors and 3D coordinates for camera relocalization
### [Project Page](https://thpjp.github.io/d2s/) | [Paper](https://arxiv.org/pdf/2307.15250.pdf)

> D2S: Representing sparse descriptors and 3D coordinates for camera relocalization                                                                                                                                                
> [Bach-Thuan Bui](https://thuanbb.github.io/), [Huy-Hoang Bui](https://github.com/AustrianOakvn), [Dinh-Tuan Tran](https://sites.google.com/view/tuantd), [Joo-Ho Lee](https://research-db.ritsumei.ac.jp/rithp/k03/resid/S000220;jsessionid=8CC0520A8C7C1F3D502596F0A07D64B0?lang=en)                   
> IEEE Robotics and Automation Letters (RAL)

- [Introduction](#introduction)
- [Installation](#installation)
- [Dataset](#dataset)
    - [Supported Datasets](#supported-datasets)
    - [Download preprocessed datasets](#Download-preprocessed-datasets)
- [Evaluation](#Evaluation-with-pre-trained-models)
- [Training](#Training)
- [Run with your own dataset](#Run-with-your-own-dataset)
- [BibTex Citation](#bibTex-citation)

## Introduction  
D2S is a learning-based visual re-localization method. It concentrates on learning to generate 3D scene coordinates from sparse descriptors extracted from a single image. Once trained for a specific sparse SfM scene, D2S can accurately estimate the camera’s position and orientation from a new image of the scene. D2S also serves as a natural filter for outliers and non-robust descriptors, resulting in an enhanced localization accuracy down the line.

This repository contains the Pytorch implementation of our papers: 
- [D2S: Representing Sparse Descriptors and 3D Coordinates for Camera Relocalization](https://thpjp.github.io/d2s/)
- [Fast and Lightweight Scene Regressor for Camera Relocalization](https://arxiv.org/abs/2212.01830) (feat2map branch)

Todo list:
- [x] release code
- [x] run with custom data 
- [x] add inference with pre-trained models

## Installation
D2S is based on PyTorch. The main framework is implemented in Python.
Python 3.9 + required packages
```
git clone https://github.com/ais-lab/d2s.git
cd d2s
conda create --name d2s python=3.9
conda activate d2s
# Refer to https://pytorch.org/get-started/previous-versions/ to install pytorch compatible with your CUDA
python -m pip install torch==1.12.0 torchvision==0.13.0 
python -m pip install -r requirements.txt
```

D2S uses [hierarchical localization toolbox](https://github.com/cvg/Hierarchical-Localization)(hloc) to label descriptors coordinates. However, we provided scripts to download all preprocessed datasets for an immediate run.

## Dataset 
### Supported Datasets
 - [7scenes](https://www.microsoft.com/en-us/research/project/rgb-d-dataset-7-scenes/)
 - [Indoor6](https://github.com/microsoft/SceneLandmarkLocalization)
 - [Cambridge Landmarks](http://mi.eng.cam.ac.uk/projects/relocalisation/#dataset)
 - [BKC Ritsumeikan](https://drive.google.com/file/d/1XEdnrFTzThruG15pW2A_1jYAxtpYbwq5/view?usp=sharing)

### Download preprocessed datasets
Please run the provided scripts to prepare and download the data which has been preprocessed by running:

7scenes
```
./prepare_scripts/seven_scenes.sh
```
Cambridge Landmarks
```
./prepare_scripts/cambridge.sh 
```
Indoor-6
```
./prepare_scripts/indoor6.sh
```

## Evaluation with pre-trained models
Please download the pre-trained models by running:
```
./prepare_scripts/download_pre_trained_models.sh
```
For example, to evaluate Indoor6 scene1:
```
python runners/eval.py --dataset indoor6 --scene scene1 -expv d2s
```

You can receive a result like this:

For pre-trained models of 7scenes, you can download it from [here](https://drive.google.com/file/d/1bQNctr2lpFVIhw2DjAmcpzwE-RdRD-yG/view?usp=sharing)

## Training
```
python runners/train.py --dataset indoor6 --scene scene1 -expv d2s
```
## Run with your own dataset
### Install COLMAP and Hloc
- Install [COLMAP](https://colmap.github.io/install.html) from source or just run:
```
sudo snap install colmap
```
- Install [Hloc](https://github.com/thuanaislab/Hierarchical-Localization)
```
git clone --recursive https://github.com/thuanaislab/Hierarchical-Localization/
cd Hierarchical-Localization/
conda activate d2s
python -m pip install -e .
cd ..
```
### Prepare your own data 
Create a ```custom_dataset``` folder as follow and put all images in ```images``` folder:
 ```
d2s
├── custom_dataset
│   ├── images
|   |   ├── image_1.jpg
|   |   ├── ...
|   |   └── image_N.jpg
├── ...
└── README.md
 ```
### Run entire pipeline on your own data
Then run this command:
 ```
./run_custom_data_pipeline.sh custom_dataset/
 ```

## BibTex Citation 
If you find this project useful, please consider citing:
```
@article{bui2024d2s,
  title={D2S: Representing sparse descriptors and 3D coordinates for camera relocalization},
  author={Bui, Bach-Thuan and Bui, Huy-Hoang and Tran, Dinh-Tuan and Lee, Joo-Ho},
  journal={IEEE Robotics and Automation Letters},
  year={2024}
}
@article{bui2022fast,
  title={Fast and Lightweight Scene Regressor for Camera Relocalization},
  author={Bui, Thuan B and Tran, Dinh-Tuan and Lee, Joo-Ho},
  journal={arXiv preprint arXiv:2212.01830},
  year={2022}
}
```

d2s

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