论文标题
边界框差异：用于完全自由度的对象检测的3D指标
Bounding Box Disparity: 3D Metrics for Object Detection With Full Degree of Freedom
论文作者
论文摘要
2D图像中对象检测的最受欢迎的评估度量是联合（IOU）的交集。 3D对象检测的IOU指标的现有实施通常忽略一个或多个自由度。在本文中，我们首先为三维边界框提供分析解决方案。作为第二个贡献，得出了体积到体积距离的封闭式解决方案。最后，提出边界框差为一个组合的正连续指标。我们将三个指标的开源实现作为独立的Python函数，以及Open3D库和ROS节点的扩展。
The most popular evaluation metric for object detection in 2D images is Intersection over Union (IoU). Existing implementations of the IoU metric for 3D object detection usually neglect one or more degrees of freedom. In this paper, we first derive the analytic solution for three dimensional bounding boxes. As a second contribution, a closed-form solution of the volume-to-volume distance is derived. Finally, the Bounding Box Disparity is proposed as a combined positive continuous metric. We provide open source implementations of the three metrics as standalone python functions, as well as extensions to the Open3D library and as ROS nodes.
