Single-point annotation in oriented object detection of remote sensing scenarios is gaining increasing attention due to its cost-effectiveness. However, due to the granularity ambiguity of points, there is a significant performance gap between previous methods and those with fully supervision. In this study, we introduce P2RBox, which employs point prompt to generate rotated box (RBox) annotation for oriented object detection. P2RBox employs the SAM model to generate high-quality mask proposals. These proposals are then refined using the semantic and spatial information from annotation points. The best masks are converted into oriented boxes based on the feature directions suggested by the model. P2RBox incorporates two advanced guidance cues: Boundary Sensitive Mask guidance, which leverages semantic information, and Centrality guidance, which utilizes spatial information to reduce granularity ambiguity. This combination enhances detection capabilities significantly. To demonstrate the effectiveness of this method, enhancements based on the baseline were observed by integrating three different detectors. Furthermore, compared to the state-of-the-art point-annotated generative method PointOBB, P2RBox outperforms by about 29% mAP (62.43% vs 33.31%) on DOTA-v1.0 dataset, which provides possibilities for the practical application of point annotations.

翻译：在遥感场景的旋转目标检测中，单点标注因其成本效益而日益受到关注。然而，由于点的粒度模糊性，现有方法与全监督方法之间存在显著的性能差距。本研究提出P2RBox，利用点提示生成旋转框标注以进行旋转目标检测。P2RBox采用SAM模型生成高质量的掩码候选区域，随后利用标注点的语义与空间信息对这些候选区域进行优化。最佳掩码根据模型特征方向建议转换为旋转框。P2RBox融合了两类先进引导线索：利用语义信息的边界敏感掩码引导，以及运用空间信息降低粒度模糊性的中心性引导。这种组合显著提升了检测性能。为验证方法的有效性，通过集成三种不同检测器在基线模型上观察到性能提升。此外，在DOTA-v1.0数据集上，相较于最先进的点标注生成方法PointOBB，P2RBox以约29%的mAP优势（62.43%对33.31%）实现超越，这为点标注的实际应用提供了可能性。