论文标题
自动驾驶的3D对象检测：一项综合调查
3D Object Detection for Autonomous Driving: A Comprehensive Survey
论文作者
论文摘要
近年来，自主驾驶一直在受到越来越多的关注，因为它的潜力减轻了驾驶员的负担并提高驾驶的安全性。在现代的自动驾驶管道中，感知系统是必不可少的组成部分，旨在准确估计周围环境的状态，并为预测和计划提供可靠的观察。 3D对象检测是智能3D对象的位置，大小和类别在自动驾驶汽车附近的位置，大小和类别，是感知系统的重要组成部分。本文回顾了自动驾驶的3D对象检测的进步。首先，我们介绍3D对象检测的背景，并讨论此任务中的挑战。其次，我们从模型和感觉输入的各个方面（包括基于激光雷达，基于摄像头和多模式检测方法）对3D对象检测的进度进行了全面调查。我们还对每类方法中的潜力和挑战提供了深入的分析。此外，我们系统地研究了3D对象检测在驾驶系统中的应用。最后，我们对3D对象检测方法进行了性能分析，并进一步总结了多年来的研究趋势，并向前景前景前进了该领域的未来方向。
Autonomous driving, in recent years, has been receiving increasing attention for its potential to relieve drivers' burdens and improve the safety of driving. In modern autonomous driving pipelines, the perception system is an indispensable component, aiming to accurately estimate the status of surrounding environments and provide reliable observations for prediction and planning. 3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving. First, we introduce the background of 3D object detection and discuss the challenges in this task. Second, we conduct a comprehensive survey of the progress in 3D object detection from the aspects of models and sensory inputs, including LiDAR-based, camera-based, and multi-modal detection approaches. We also provide an in-depth analysis of the potentials and challenges in each category of methods. Additionally, we systematically investigate the applications of 3D object detection in driving systems. Finally, we conduct a performance analysis of the 3D object detection approaches, and we further summarize the research trends over the years and prospect the future directions of this area.
