PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection
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arXiv
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908377469157376 |
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| author | Zhang, Peiyuan Luo, Junwei Yang, Xue Yu, Yi Li, Qingyun Zhou, Yue Jia, Xiaosong Lu, Xudong Chen, Jingdong Li, Xiang Yan, Junchi Li, Yansheng |
| author_facet | Zhang, Peiyuan Luo, Junwei Yang, Xue Yu, Yi Li, Qingyun Zhou, Yue Jia, Xiaosong Lu, Xudong Chen, Jingdong Li, Xiang Yan, Junchi Li, Yansheng |
| contents | With the growing demand for oriented object detection (OOD), recent studies on point-supervised OOD have attracted significant interest. In this paper, we propose PointOBB-v3, a stronger single point-supervised OOD framework. Compared to existing methods, it generates pseudo rotated boxes without additional priors and incorporates support for the end-to-end paradigm. PointOBB-v3 functions by integrating three unique image views: the original view, a resized view, and a rotated/flipped (rot/flp) view. Based on the views, a scale augmentation module and an angle acquisition module are constructed. In the first module, a Scale-Sensitive Consistency (SSC) loss and a Scale-Sensitive Feature Fusion (SSFF) module are introduced to improve the model's ability to estimate object scale. To achieve precise angle predictions, the second module employs symmetry-based self-supervised learning. Additionally, we introduce an end-to-end version that eliminates the pseudo-label generation process by integrating a detector branch and introduces an Instance-Aware Weighting (IAW) strategy to focus on high-quality predictions. We conducted extensive experiments on the DIOR-R, DOTA-v1.0/v1.5/v2.0, FAIR1M, STAR, and RSAR datasets. Across all these datasets, our method achieves an average improvement in accuracy of 3.56% in comparison to previous state-of-the-art methods. The code will be available at https://github.com/ZpyWHU/PointOBB-v3. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2501_13898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection Zhang, Peiyuan Luo, Junwei Yang, Xue Yu, Yi Li, Qingyun Zhou, Yue Jia, Xiaosong Lu, Xudong Chen, Jingdong Li, Xiang Yan, Junchi Li, Yansheng Computer Vision and Pattern Recognition Artificial Intelligence With the growing demand for oriented object detection (OOD), recent studies on point-supervised OOD have attracted significant interest. In this paper, we propose PointOBB-v3, a stronger single point-supervised OOD framework. Compared to existing methods, it generates pseudo rotated boxes without additional priors and incorporates support for the end-to-end paradigm. PointOBB-v3 functions by integrating three unique image views: the original view, a resized view, and a rotated/flipped (rot/flp) view. Based on the views, a scale augmentation module and an angle acquisition module are constructed. In the first module, a Scale-Sensitive Consistency (SSC) loss and a Scale-Sensitive Feature Fusion (SSFF) module are introduced to improve the model's ability to estimate object scale. To achieve precise angle predictions, the second module employs symmetry-based self-supervised learning. Additionally, we introduce an end-to-end version that eliminates the pseudo-label generation process by integrating a detector branch and introduces an Instance-Aware Weighting (IAW) strategy to focus on high-quality predictions. We conducted extensive experiments on the DIOR-R, DOTA-v1.0/v1.5/v2.0, FAIR1M, STAR, and RSAR datasets. Across all these datasets, our method achieves an average improvement in accuracy of 3.56% in comparison to previous state-of-the-art methods. The code will be available at https://github.com/ZpyWHU/PointOBB-v3. |
| title | PointOBB-v3: Expanding Performance Boundaries of Single Point-Supervised Oriented Object Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2501.13898 |