In this paper, a single-stage 3D object detection framework, 3D-CenterNet, is proposed for accurate 3D object detection from point clouds. We find that the center position is more critical for accurate bounding box detection than the other two parameters, the size and the orientation. Motivated by this discovery, we propose the center regression module (CRM) to regress the centers’ location from the point-wise features. In CRM, the representative points belonging to objects are sampled to regress the center locations of the corresponding objects. The semantic and geometric information related to the estimated centers is aggregated for the following location refinement and other parameters’ estimation. The 3D-CenterNet stacks the CRMs to improve the accuracy of the estimated centers gradually. The size and orientation of the bounding boxes are decoded from the high dimensional center-wise features. The experiments on the KITTI benchmark and the SUN RGB-D datasets show that our proposed 3D-CenterNet achieves high-quality results in real time.

本文提出了一种单阶段3D对象检测框架3D-CenterNet，用于从点云中进行精确的3D对象检测。我们发现，中心位置对于精确的边界框检测比其他两个参数（大小和方向）更为关键。受此发现的启发，我们提出了中心回归模块（CRM）从点状要素回归中心的位置。在CRM中，对属于对象的代表点进行采样以使相应​​对象的中心位置回归。与估计的中心有关的语义和几何信息被汇总，用于随后的位置细化和其他参数的估计。3D-CenterNet堆叠了CRM，以逐步提高估计中心的准确性。边界框的大小和方向是从高维中心方向特征中解码的。在KITTI基准测试和SUN RGB-D数据集上进行的实验表明，我们提出的3D-CenterNet可以实时获得高质量的结果。