Object pose detection and tracking has recently attracted increasing attention due to its wide applications in many areas, such as autonomous driving, robotics, and augmented reality. Among methods for object pose detection and tracking, deep learning is the most promising one that has shown better performance than others. However, survey study about the latest development of deep learning-based methods is lacking. Therefore, this study presents a comprehensive review of recent progress in object pose detection and tracking that belongs to the deep learning technical route. To achieve a more thorough introduction, the scope of this study is limited to methods taking monocular RGB/RGBD data as input and covering three kinds of major tasks: instance-level monocular object pose detection, category-level monocular object pose detection, and monocular object pose tracking. In our work, metrics, datasets, and methods of both detection and tracking are presented in detail. Comparative results of current state-of-the-art methods on several publicly available datasets are also presented, together with insightful observations and inspiring future research directions.

由于其在自动驾驶、机器人和增强现实等许多领域的广泛应用，物体姿态检测和跟踪最近引起了越来越多的关注。在物体姿态检测和跟踪的方法中，深度学习是最有前途的一种，其性能优于其他方法。然而，缺乏关于基于深度学习的方法的最新发展的调查研究。因此，本研究对属于深度学习技术路线的目标姿态检测和跟踪的最新进展进行了全面回顾。为了实现更全面的介绍，本研究的范围仅限于以单目 RGB/RGBD 数据为输入的方法，涵盖三种主要任务：实例级单目物体姿态检测、类别级单目物体姿态检测、和单眼物体姿态跟踪。在我们的工作中，详细介绍了检测和跟踪的指标、数据集和方法。还介绍了当前最先进的方法在几个公开可用的数据集上的比较结果，以及有见地的观察和启发未来的研究方向。