论文标题
COFF：用于自动驾驶汽车3D对象检测的合作空间特征融合
CoFF: Cooperative Spatial Feature Fusion for 3D Object Detection on Autonomous Vehicles
论文作者
论文摘要
为了减少传输数据的量，最近提出了基于特征图的融合作为对自动驾驶汽车合作3D对象检测的实用解决方案。但是，对象检测的精度可能需要显着改进，尤其是对于遥远或遮挡的对象。为了解决自动驾驶汽车和人类安全性的关键问题，我们为自动驾驶汽车提出了一种合作的空间特征融合（COFF）方法，以有效地融合特征图，以实现更高的3D对象检测性能。特别是，根据收到的功能地图提供了多少新的语义信息，Coff将特征图之间的权重区分了更具指导性的融合。它还增强了与远/遮挡对象相对应的不起眼的特征，以提高其检测精度。实验结果表明，与先前的特征融合溶液相比，Coff在自动驾驶汽车的检测精度和有效检测范围方面取得了显着改善。
To reduce the amount of transmitted data, feature map based fusion is recently proposed as a practical solution to cooperative 3D object detection by autonomous vehicles. The precision of object detection, however, may require significant improvement, especially for objects that are far away or occluded. To address this critical issue for the safety of autonomous vehicles and human beings, we propose a cooperative spatial feature fusion (CoFF) method for autonomous vehicles to effectively fuse feature maps for achieving a higher 3D object detection performance. Specially, CoFF differentiates weights among feature maps for a more guided fusion, based on how much new semantic information is provided by the received feature maps. It also enhances the inconspicuous features corresponding to far/occluded objects to improve their detection precision. Experimental results show that CoFF achieves a significant improvement in terms of both detection precision and effective detection range for autonomous vehicles, compared to previous feature fusion solutions.
