自动驾驶中基于深度学习的3D目标检测方法综述

随着激光雷达传感器和深度学习技术的快速发展，针对自动驾驶3D目标检测算法的研究呈现爆发式增长。为了探究3D目标检测技术的发展和演变，对该领域中基于深度学习的3D检测算法进行了综述。根据车载传感器的不同，将当前基于深度学习的自动驾驶3D目标检测算法分为基于相机RGB图像、基于激光雷达点云、基于RGB图像–激光雷达点云融合的3D目标检测3种类型。在此基础上，分析了各类算法的技术原理及其发展历程，并根据平均检测精度（mAP）指标，对比了它们的性能差异与模型优缺点。最后，总结和展望了当前自动驾驶3D目标检测中仍然面临的技术挑战及未来发展趋势。

Survey on deep learning-based 3D object detection methods in autonomous driving

With the rapid developments of LiDAR sensors and deep learning technology, researches on 3D object detection for autonomous driving had witnessed remarkable growth. In order to explore the evolution of 3D object detection technology in autonomous driving, the existing deep learning-based 3D object detection methods were summarized. Based on the rely-on sensors, these methods could be divided into three classes: Camera RGB image-based，LiDAR point cloud-based and RGB image-LiDAR point cloud fusion-based 3D object detection. On this basis, the methodology and chronological overview of different kinds of methods were analyzed, and their 3D detection performance and characteristics were compared according to the mean average precision (mAP) metrics. Finally, the principal technical challenges and potential development trends in future autonomous driving 3D object detection researches were summarized and explored.