Introduced the 3D object tracking technology and its latest research progress, including the theoretical foundation and evaluation metrics, traditional pose estimation based on visual features, and learning-based pose estimation. Analyzed various feature constraints, statistical segmentation models, energy functions, and acceleration strategies in traditional algorithms. Region and edge constraints become the mainstream, and fused features can produce stronger constraints. The statistical segmentation model has gradually developed from a global model to local models. The energy function in the form of nonlinear least squares is easy to optimize and is widely used. Pre-computation and pre-rendering acceleration strategies greatly improve the real-time performance. Learning-based algorithms are not as accurate and fast as traditional algorithms, but show greater potential of feature extraction and handling more complex scenes. Summarized tracking accuracy of various algorithms and the reasons for high accuracy. Reviewed the problems of tracking accuracy decline and failure in complex situations, as well as potential solutions and development directions. 3D object tracking is moving towards the direction of multi-feature fusion, pre-computation, and multitasking.