  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.
