论文标题
SIANMS：用于多相机3D对象检测的暹罗网络的非最大抑制
siaNMS: Non-Maximum Suppression with Siamese Networks for Multi-Camera 3D Object Detection
论文作者
论文摘要
自动驾驶汽车中嵌入式硬件的快速开发扩大了其计算能力，从而使更完整的传感器设置具有能够处理更高复杂性的驾驶场景的可能性。结果，必须解决新的挑战，例如对同一对象的多个检测。在这项工作中，将暹罗网络集成到了众所周知的3D对象检测器方法的管道中，以抑制通过重新识别来自不同相机的重复提案。此外，利用关联来通过汇总其相应的LIDAR冰果来增强对象的3D盒回归。 Nuscenes数据集的实验评估表明，所提出的方法的表现优于传统的NMS方法。
The rapid development of embedded hardware in autonomous vehicles broadens their computational capabilities, thus bringing the possibility to mount more complete sensor setups able to handle driving scenarios of higher complexity. As a result, new challenges such as multiple detections of the same object have to be addressed. In this work, a siamese network is integrated into the pipeline of a well-known 3D object detector approach to suppress duplicate proposals coming from different cameras via re-identification. Additionally, associations are exploited to enhance the 3D box regression of the object by aggregating their corresponding LiDAR frustums. The experimental evaluation on the nuScenes dataset shows that the proposed method outperforms traditional NMS approaches.
