3D object detection is a critical part of environmental perception systems and one of the most fundamental tasks in understanding the 3D visual world, which benefit a series of downstream real-world applications. RGB-D images include object texture and semantic information, as well as depth information describing spatial geometry. Recently, numerous 3D object detection models for RGB-D images have been proposed with excellent performance, but summaries in this area are still absent. To stimulate future research, this paper provides a detailed analysis of current developments in 3D object detection methods for RGB-D images to motivate future research. It covers three major parts, including background on 3D object detection, RGB-D data details, and comparative results of state-of-the-art methods on several publicly available datasets, with an emphasis on contributions, design ideas, and limitations, as well as insightful observations and inspiring future research directions.

3D 物体检测是环境感知系统的关键部分，也是理解 3D 视觉世界的最基本任务之一，它有利于一系列下游的现实世界应用。RGB-D 图像包括对象纹理和语义信息，以及描述空间几何的深度信息。最近，已经提出了许多用于 RGB-D 图像的 3D 对象检测模型，性能优异，但仍然缺乏这方面的总结。为了刺激未来的研究，本文详细分析了 RGB-D 图像的 3D 对象检测方法的当前发展，以激发未来的研究。它涵盖了三个主要部分，包括 3D 对象检测的背景、RGB-D 数据细节以及在几个公开可用的数据集上的最新方法的比较结果，