This paper provides a comprehensive review of 6DoF object pose estimation methods, focusing on advancements in deep learning. The task involves extracting target areas from input data and determining the position and orientation of objects. The review categorizes methods into instance-level and category-level groups, based on whether CAD models are required. Recent progress in learning-based 6DoF pose estimation is discussed, covering various methods, datasets, and application scenarios. The study systematically explores innovations and applicable scenarios, comparing state-of-the-art methods across public datasets, considering differences in input data types. Challenges, methods for different applications, and future directions are also highlighted. The paper aims to help researchers better understand the latest developments in 6DoF object pose estimation, addressing issues such as occlusion, lighting variations, and real-time performance.This paper provides a comprehensive review of 6DoF object pose estimation methods, focusing on advancements in deep learning. The task involves extracting target areas from input data and determining the position and orientation of objects. The review categorizes methods into instance-level and category-level groups, based on whether CAD models are required. Recent progress in learning-based 6DoF pose estimation is discussed, covering various methods, datasets, and application scenarios. The study systematically explores innovations and applicable scenarios, comparing state-of-the-art methods across public datasets, considering differences in input data types. Challenges, methods for different applications, and future directions are also highlighted. The paper aims to help researchers better understand the latest developments in 6DoF object pose estimation, addressing issues such as occlusion, lighting variations, and real-time performance.