  Differing from the well-developed horizontal object detection area whereby
the computing-friendly IoU based loss is readily adopted and well fits with the
detection metrics. In contrast, rotation detectors often involve a more
complicated loss based on SkewIoU which is unfriendly to gradient-based
training. In this paper, we propose an effective approximate SkewIoU loss based
on Gaussian modeling and Gaussian product, which mainly consists of two items.
The first term is a scale-insensitive center point loss, which is used to
quickly narrow the distance between the center points of the two bounding
boxes. In the distance-independent second term, the product of the Gaussian
distributions is adopted to inherently mimic the mechanism of SkewIoU by its
definition, and show its alignment with the SkewIoU loss at trend-level within
a certain distance (i.e. within 9 pixels). This is in contrast to recent
Gaussian modeling based rotation detectors e.g. GWD loss and KLD loss that
involve a human-specified distribution distance metric which require additional
hyperparameter tuning that vary across datasets and detectors. The resulting
new loss called KFIoU loss is easier to implement and works better compared
with exact SkewIoU loss, thanks to its full differentiability and ability to
handle the non-overlapping cases. We further extend our technique to the 3-D
case which also suffers from the same issues as 2-D. Extensive results on
various public datasets (2-D/3-D, aerial/text/face images) with different base
detectors show the effectiveness of our approach.
