# NeuralMarker

**Repository Path**: abunch/NeuralMarker

## Basic Information

- **Project Name**: NeuralMarker
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-09-23
- **Last Updated**: 2025-09-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# NeuralMarker
### [Project Page](https://drinkingcoder.github.io/publication/neuralmarker/)

> NeuralMarker: A Framework for Learning General Marker Correspondence   
> [Zhaoyang Huang](https://drinkingcoder.github.io)<sup>\*</sup>, Xiaokun Pan<sup>\*</sup>, Weihong Pan, Weikang Bian, [Yan Xu](https://decayale.github.io/), Ka Chun Cheung, [Guofeng Zhang](http://www.cad.zju.edu.cn/home/gfzhang/), [Hongsheng Li](https://www.ee.cuhk.edu.hk/~hsli/)  
> SIGGRAPH Asia (ToG) 2022  

## TODO List
- [x] Code release
- [x] Models release 
- [x] Demo code release 
- [x] Dataset&Evaluation code release 

## Environment
```
conda create -n neuralmarker
conda activate neuralmarker
conda install python=3.7
pip install -r requirements.txt
```

## Dataset

We use the MegaDepth dataset that preprocessed by [CAPS](https://github.com/qianqianwang68/caps), which is provided in this [link](https://drive.google.com/file/d/1-o4TRLx6qm8ehQevV7nExmVJXfMxj657/view?usp=sharing).
We generate FlyingMarkers training set online. To genenerate FlyingMarkers validation set and test set, please execute:
```
python synthesis_datasets.py --root ./data/MegaDepth_CAPS/ --csv ./data/synthesis_validate_release.csv --save_dir ./data/flyingmarkers/validation
python synthesis_datasets.py --root ./data/MegaDepth_CAPS/ --csv ./data/synthesis_validate_short.csv --save_dir ./data/validation/synthesis
python synthesis_datasets.py --root ./data/MegaDepth_CAPS/ --csv ./data/synthesis_test_release.csv --save_dir ./data/flyingmarkers/test
```

The pretrained models, DVL-Markers benchmark, and data for demo are stored in [Google Drive](https://drive.google.com/drive/folders/1PZvFhx9P3TJZEiLowav-al0hhSH3hxrh?usp=share_link).


## Training
We train our model on 6 V100 with batch size 2.
```
CUDA_VISIBLE_DEVICES=0,1,2,3,4,5 python train.py
```

## DVL-Markers Evaluation
Put the DVL-Markers dataset in `data`:
```
├── data 
    ├── DVL
        ├── D
        ├── V
        ├── L
        ├── marker
```
then run
```
bash eval_DVL.sh
```
The results will be saved in `output`

## FlyingMarkers Evaluation
```
python evaluation_FM.py
```

## Demo
for video demo, run
```
bash demo_video.sh
```


## Acknowledgements
We thank Yijin Li, Rensen Xu, and Jundan Luo for their help.
We refer DGC-Net to generate synthetic image pairs.