  With the rapidly increasing demand for oriented object detection (OOD),
recent research involving weakly-supervised detectors for learning rotated box
(RBox) from the horizontal box (HBox) has attracted more and more attention. In
this paper, we explore a more challenging yet label-efficient setting, namely
single point-supervised OOD, and present our approach called Point2RBox.
Specifically, we propose to leverage two principles: 1) Synthetic pattern
knowledge combination: By sampling around each labeled point on the image, we
spread the object feature to synthetic visual patterns with known boxes to
provide the knowledge for box regression. 2) Transform self-supervision: With a
transformed input image (e.g. scaled/rotated), the output RBoxes are trained to
follow the same transformation so that the network can perceive the relative
size/rotation between objects. The detector is further enhanced by a few
devised techniques to cope with peripheral issues, e.g. the anchor/layer
assignment as the size of the object is not available in our point supervision
setting. To our best knowledge, Point2RBox is the first end-to-end solution for
point-supervised OOD. In particular, our method uses a lightweight paradigm,
yet it achieves a competitive performance among point-supervised alternatives,
41.05%/27.62%/80.01% on DOTA/DIOR/HRSC datasets.
