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.

翻译：近年来，自动驾驶因其在减轻驾驶员负担与提升行车安全方面的潜力而受到日益广泛的关注。在现代自动驾驶流程中，感知系统作为不可或缺的组成部分，旨在准确估计周围环境状态，并为预测与规划提供可靠观测数据。三维目标检测作为感知系统的重要环节，能够智能预测自动驾驶车辆附近关键三维目标的位置、尺寸及类别。本文回顾了面向自动驾驶的三维目标检测研究进展。首先，我们介绍了三维目标检测的背景，并探讨了该任务面临的挑战。其次，我们从模型与传感器输入的角度，对三维目标检测的进展进行了全面综述，涵盖基于激光雷达、基于相机以及多模态检测方法。我们还深入分析了每类方法的潜力与挑战。此外，我们系统研究了三维目标检测在驾驶系统中的应用。最后，我们对三维目标检测方法进行了性能分析，进一步总结了历年来的研究趋势，并展望了该领域的未来方向。