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license: apache-2.0
---
# Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection
[](https://arxiv.org/pdf/2506.10601)
[](https://github.com/antxinyuan/ssp)
🔥 We appreciate the attention to our paper. The code is available at [Github repo](https://github.com/antxinyuan/ssp).
> Production from Institute of Computing Technology, Chinese Academy of Sciences.
> Primary contact: Xinyuan Liu ( liuxinyuan21s@ict.ac.cn ).
## TL;DR
This repository contains the source code of [**Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection**](https://arxiv.org/pdf/2506.10601).
To tackle inadequate sample assignment and instance confusion in point-supervised oriented object detection for remote sensing dense scenes, we propose SSP (Semantic-decoupled Spatial Partition), a framework integrating rule-driven prior injection and data-driven label purification. Its core innovations include pixel-level spatial partition for sample assignment and semantic-modulated box extraction for pseudo-label generation.
### Pseudo-label performance
All pseudo-labeling results are available in [pseudo_labels](https://huggingface.co/antxinyuan/SSP/blob/main/pseudo_labels.zip).
| Dataset | mAP | mIoU | ann_file |
| :-: | :-: | :-: | :-: |
| DOTA-v1.0 | 34.95 |49.03 | pseudo_labels/ssp_dotav10_hybrid/ |
| DOTA-v1.5 | 28.89 | 44.92 | pseudo_labels/ssp_dotav15_hybrid/ |
| DOTA-v2.0 | 24.72 | 41.93 | pseudo_labels/ssp_dotav20_hybrid/ |
### Detectors performance
| Dataset | Config | Log | Checkpoint | mAP(paper) | mAP(reproduced) |
| :-: | :-: | :-: | :-: | :-: | :-: |
| SSP(RFOCS) | [config](https://github.com/antxinyuan/ssp/blob/main/configs/ssp/rfcos_ssp_dotav10.py) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/logs/rfcos_ssp_dotav10.json) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/models/rfcos_ssp_dotav10-4c17ff33.pth) | 45.78 | 45.82 |
| SSP(ORCNN) | [config](https://github.com/antxinyuan/ssp/blob/main/configs/ssp/orcnn_ssp_dotav10.py) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/logs/orcnn_ssp_dotav10.json) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/models/orcnn_ssp_dotav10-2df034d3.pth) | 47.86 | 48.81 |
| SSP(ReDet) | [config](https://github.com/antxinyuan/ssp/blob/main/configs/ssp/orcnn_ssp_dotav20.py) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/logs/redet_ssp_dotav10.json) | [hugging face](https://huggingface.co/antxinyuan/SSP/blob/main/models/redet_ssp_dotav10-eed2738e.pth) | 48.50 | 49.02 |
## 🖊️ Citation
If you find this work helpful for your research, please consider giving this repo a star ⭐ and citing our papers:
## Citation
If this work is helpful for your research, please consider citing the following BibTeX entry.
``` bibtex
@misc{liu2025ssp,
title={Semantic-decoupled Spatial Partition Guided Point-supervised Oriented Object Detection},
author={Xinyuan Liu and Hang Xu and Yike Ma and Yucheng Zhang and Feng Dai},
year={2025},
eprint={2506.10601},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2506.10601},
}
@inproceedings{xu2024acm,
title={Rethinking boundary discontinuity problem for oriented object detection},
author={Xu, Hang and Liu, Xinyuan and Xu, Haonan and Ma, Yike and Zhu, Zunjie and Yan, Chenggang and Dai, Feng},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={17406--17415},
year={2024}
}
```
## Related resources
We acknowledge all the open-source contributors for the following projects to make this work possible:
- [PointOBB-v2](https://github.com/VisionXLab/PointOBB-v2)
- [MMRotate](https://github.com/open-mmlab/mmrotate)
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