Oriented object detection, a specialized subfield in computer vision, finds
applications across diverse scenarios, excelling particularly when dealing with
objects of arbitrary orientations. Conversely, point annotation, which treats
objects as single points, offers a cost-effective alternative to rotated and
horizontal bounding boxes but sacrifices performance due to the loss of size
and orientation information. In this study, we introduce the P2RBox network,
which leverages point annotations and a mask generator to create mask
proposals, followed by filtration through our Inspector Module and Constrainer
Module. This process selects high-quality masks, which are subsequently
converted into rotated box annotations for training a fully supervised
detector. Specifically, we've thoughtfully crafted an Inspector Module rooted
in multi-instance learning principles to evaluate the semantic score of masks.
We've also proposed a more robust mask quality assessment in conjunction with
the Constrainer Module. Furthermore, we've introduced a Symmetry Axis
Estimation (SAE) Module inspired by the spectral theorem for symmetric matrices
to transform the top-performing mask proposal into rotated bounding boxes.
P2RBox performs well with three fully supervised rotated object detectors:
RetinaNet, Rotated FCOS, and Oriented R-CNN. By combining with Oriented R-CNN,
P2RBox achieves 62.26% on DOTA-v1.0 test dataset. As far as we know, this is
the first attempt at training an oriented object detector with point
supervision.

我们介绍了 P2RBox 网络，它利用点注释和掩膜生成器创建掩膜提案，并通过我们的检查器模块和约束器模块进行过滤，从而选择高质量的掩膜，并将其转换为旋转边界框注释以训练一个全监督检测器。这是第一次尝试使用点监督训练定向物体检测器。