3D目标检测进展综述

Advances in 3D Object Detection:A Brief Survey

目标检测算法应用广泛,一直是计算机视觉领域备受关注的研究热点。近年来,随着深度学习的发展,3D图像的目标检测研究取得了巨大的突破。与2D目标检测相比,3D目标检测结合了深度信息,能够提供目标的位置、方向和大小等空间场景信息,在自动驾驶和机器人领域发展迅速。文中首先对基于深度学习的2D目标检测算法进行概述;其次根据图像、激光雷达、多传感器等不同数据采集方式,分析目前具有代表性和开创性的3D目标检测算法;结合自动驾驶的应用场景,对比分析不同3D目标检测算法的性能、优势和局限性;最后总结了3D目标检测的应用意义以及待解决的问题,并对3D目标检测的发展方向和新的挑战进行了讨论和展望。

Object detection is useful in many application scenarios,and is one of the most important research topics in computer vision.In recent years,with the development of deep learning,3D object detection has achieved significant breakthrough.Compared with 2D object detection,3D object detection can provide space scene information such as location,orientation and size of interest object,which plays an important role in autonomous driving and robot research.This paper firstly summarized deep lear-ning-based 2D object detection,then reviewed recent novel 3D object detection algorithms based on different data type of image,point cloud and multi-sensors,and analyzd performances,advantages and limitations of typical 3D object detection algorithms in autonomous driving scenario.Finally,this paper summarized the application direction and research topics and challenges of 3D object detection.