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[ECCV2024] Occlusion-Aware Seamless Segmentation

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<p align="center">
<h1 align="center"><strong>Occlusion-Aware Seamless Segmentation</strong></h1>
<h3 align="center">ECCV 2024</h3>

<p align="center">
    <a>Yihong Cao</a><sup>1*</sup>,</span>
    <a href="https://jamycheung.github.io">Jiaming Zhang</a><sup>2*</sup>,
    <a>Hao Shi</a><sup>3</sup>,
    <a>Kunyu Peng</a><sup>2</sup>,
    <a>Yuhongxuan Zhang</a><sup>1</sup>,
    <a href="http://robotics.hnu.edu.cn/info/1176/2966.htm">Hui Zhang</a><sup>1†</sup>,
    <a href="https://cvhci.anthropomatik.kit.edu/~stiefel">Rainer Stiefelhagen</a><sup>2</sup>,
    <a href="https://yangkailun.com">Kailun Yang</a><sup>1†</sup>
    <br>
        <sup>1</sup>Hunan University,
        <sup>2</sup>Karlsruhe Institute of Technology,
        <sup>3</sup>Zhejiang University
</p>

## OASS [[PDF]](https://arxiv.org/pdf/2407.02182)
<div align="left">
  <img src="Img/OASS_Task.png" width="500"/>
</div>

## Datasets
### [BlendPASS](https://github.com/yihong-97/BlendPASS)
Considering the scalability of future work, we annotated 100 images containing 2,960 objects across various classes in the evaluation set, following the format of Cityscapes. For more details, please visit the [BlendPASS homepage](https://github.com/yihong-97/BlendPASS) or download directly [here](https://drive.google.com/drive/folders/1t-dUjH4zeu4fBhtr6AbKULjnbSWKje0S?usp=sharing).
<div align="left">
  <img src="Img/BlendPASS.png" width="600"/>
</div>

### Conversion to BlendPASS Format
Due to inconsistent class structures between BlendPASS (8 classes) and the source datasets (KITTI-360 APS and AmodalSynthDrive, each with 7 classes), we coarsely aligned the class labels to enable cross-dataset compatibility. The converted datasets are available for download:
- KITTI-360 APS → BlendPASS: Download [here](https://drive.google.com/drive/folders/1T0GDze9s-aQw86PFzrKCSkxx4Un_Kd-A?usp=sharing)
- AmodalSynthDrive → BlendPASS: Download [here](https://drive.google.com/drive/folders/1kGSEHhBHGeRJEtfpYHXye3N3Z8z3eHH5?usp=sharing)

## Usage
### Installation
```
pytorch==1.7.1
torchvision==0.8.2
torchaudio==0.7.2
cudatoolkit=10.2
mmcv-full==1.6.2
```
To facilitate your testing, we have packaged our dependencies. You can download them [here]() and extract directly into your Anaconda virtual environment folder.

### Datasets
**Source:** KITTI360-APS

Please refer to the [Homepage](https://amodal-panoptic.cs.uni-freiburg.de/) of KITTI360-APS.

**Target:** BlendPASS

### Train
```shell
python run_experiments.py --config configs/unmaskformer/Training_OASS_UnmaskFormer.py
```

### Evaluation
Our pretrained model is available at [here]().
```shell
python run_experiments.py --config configs/unmaskformer/Testing_UnmaskFormer.py
```

## 🤝 Publication:
Please consider referencing this paper if you use the ```code``` or ```data``` from our work.
Thanks a lot :)

```
@inproceedings{cao2024oass,
  title={Occlusion-Aware Seamless Segmentation},
  author={Yihong Cao and Jiaming Zhang and Hao Shi and Kunyu Peng and Yuhongxuan Zhang and Hui Zhang and Rainer Stiefelhagen and Kailun Yang},
  booktitle={European Conference on Computer Vision (ECCV)},
  year={2024}
}
```
## Acknowledgement:

This project is based on the following open-source projects. We thank their
authors for making the source code publically available.

* [DAFormer](https://github.com/lhoyer/DAFormer)
* [MMDetection](https://github.com/open-mmlab/mmdetection)
* [MMSegmentation](https://github.com/open-mmlab/mmsegmentation)
* [EDAPS](https://github.com/susaha/edaps)

## License

This project is released under the [Apache License 2.0](LICENSE), while some 
specific features in this repository are with other licenses. Please refer to 
[LICENSES.md](LICENSES.md) for the careful check, if you are using our code for 
commercial matters.

OASS

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