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.

该论文介绍了面向对象检测的点轴表示方法，强调其灵活性和几何直观性，其关键组成部分包括点和轴，点描述对象的空间范围和轮廓，轴定义对象的主方向，解决了传统基于边界框的方法中常见的位置和旋转不连续问题，实验证明该方法在面向对象检测任务中显著提高了性能。

基于点-轴表示的面向对象检测的点投影方法