  This paper introduces the point-axis representation for oriented object
detection, emphasizing its flexibility and geometrically intuitive nature with
two key components: points and axes. 1) Points delineate the spatial extent and
contours of objects, providing detailed shape descriptions. 2) Axes define the
primary directionalities of objects, providing essential orientation cues
crucial for precise detection. The point-axis representation decouples location
and rotation, addressing the loss discontinuity issues commonly encountered in
traditional bounding box-based approaches. For effective optimization without
introducing additional annotations, we propose the max-projection loss to
supervise point set learning and the cross-axis loss for robust axis
representation learning. Further, leveraging this representation, we present
the Oriented DETR model, seamlessly integrating the DETR framework for precise
point-axis prediction and end-to-end detection. Experimental results
demonstrate significant performance improvements in oriented object detection
tasks.
