Camera, LiDAR and radar are common perception sensors for autonomous driving
tasks. Robust prediction of 3D object detection is optimally based on the
fusion of these sensors. To exploit their abilities wisely remains a challenge
because each of these sensors has its own characteristics. In this paper, we
propose FADet, a multi-sensor 3D detection network, which specifically studies
the characteristics of different sensors based on our local featured attention
modules. For camera images, we propose dual-attention-based sub-module. For
LiDAR point clouds, triple-attention-based sub-module is utilized while
mixed-attention-based sub-module is applied for features of radar points. With
local featured attention sub-modules, our FADet has effective detection results
in long-tail and complex scenes from camera, LiDAR and radar input. On NuScenes
validation dataset, FADet achieves state-of-the-art performance on LiDAR-camera
object detection tasks with 71.8% NDS and 69.0% mAP, at the same time, on
radar-camera object detection tasks with 51.7% NDS and 40.3% mAP. Code will be
released at this https URL

基于多传感器融合和局部特征注意力模块，本文提出了 FADet，一种用于自动驾驶任务的多传感器 3D 检测网络，能够在相机、LiDAR 和雷达输入下的长尾和复杂场景中实现有效的目标检测结果，并在 LiDAR - 相机和雷达 - 相机目标检测任务上达到了最先进的性能。