  Single point-supervised object detection is gaining attention due to its
cost-effectiveness. However, existing approaches focus on generating horizontal
bounding boxes (HBBs) while ignoring oriented bounding boxes (OBBs) commonly
used for objects in aerial images. This paper proposes PointOBB, the first
single Point-based OBB generation method, for oriented object detection.
PointOBB operates through the collaborative utilization of three distinctive
views: an original view, a resized view, and a rotated/flipped (rot/flp) view.
Upon the original view, we leverage the resized and rot/flp views to build a
scale augmentation module and an angle acquisition module, respectively. In the
former module, a Scale-Sensitive Consistency (SSC) loss is designed to enhance
the deep network's ability to perceive the object scale. For accurate object
angle predictions, the latter module incorporates self-supervised learning to
predict angles, which is associated with a scale-guided Dense-to-Sparse (DS)
matching strategy for aggregating dense angles corresponding to sparse objects.
The resized and rot/flp views are switched using a progressive multi-view
switching strategy during training to achieve coupled optimization of scale and
angle. Experimental results on the DIOR-R and DOTA-v1.0 datasets demonstrate
that PointOBB achieves promising performance, and significantly outperforms
potential point-supervised baselines.
