We present an open-source toolbox, named MMRotate, which provides a coherent algorithm
framework of training, inferring, and evaluation for the popular rotated object detection …

Boundary discontinuity and its inconsistency to the final detection metric have been the
bottleneck for rotating detection regression loss design. In this paper, we propose a novel …

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 …

In this paper, we study the problem of unsupervised object detection from 3D point clouds in
self-driving scenes. We present a simple yet effective method that exploits (i) point clustering …

Oriented object detection (OOD) aims to precisely detect the objects with arbitrary orientation
in remote sensing images (RSIs). Up to now, most of the bounding box regression (BBR) …

Arbitrary-oriented objects exist widely in remote sensing images. The mainstream rotation
detectors use oriented bounding boxes (OBBs) or quadrilateral bounding boxes (QBBs) to …

Abstract Since Intersection-over-Union (IoU) based optimization maintains the consistency
of the final IoU prediction metric and losses, it has been widely used in both regression and …

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 …

Real-time and high-performance 3D object detection is an attractive research direction in
autonomous driving. Recent studies prefer point based or voxel based convolution for …

Existing oriented object detection methods commonly use metric AP $ _ {50} $ to measure
the performance of the model. We argue that AP $ _ {50} $ is inherently unsuitable for …