Detecting Rotated Objects as Gaussian Distributions and Its 3-D Generalization. (arXiv:2209.10839v1 [cs.CV])

Existing detection methods commonly use a parameterized bounding box (BBox)
to model and detect (horizontal) objects and an additional rotation angle
parameter is used for rotated objects. We argue that such a mechanism has
fundamental limitations in building an effective regression loss for rotation
detection, especially for high-precision detection with high IoU (e.g. 0.75).
Instead, we propose to model the rotated objects as Gaussian distributions. A
direct advantage is that our new regression loss regarding the distance between
two Gaussians …

3-d arxiv objects