This paper provides a comprehensive survey of 6DoF object pose estimation methods for different application scenarios. The task involves determining the position and orientation of objects in real-world scenes, which is crucial for applications such as virtual reality, augmented reality, autonomous driving, and robotic operations. The paper categorizes existing methods into instance-level and category-level approaches, based on whether the CAD model of the object is required. It reviews recent advancements in learning-based 6DoF pose estimation methods, exploring their innovations and applicable scenarios. The study provides an overview of widely used datasets, task metrics, and diverse application scenarios. It also compares state-of-the-art methods across publicly accessible datasets, taking into account differences in input data types. The paper summarizes the challenges of current tasks, methods for different applications, and future development directions. The paper discusses various methods for 6DoF object pose estimation, including RGB-based, point cloud or depth-based, and RGB-D-based methods. RGB-based methods use color images to estimate object poses, while point cloud or depth-based methods use depth information or point clouds. RGB-D-based methods combine RGB and depth information to enhance the ability to extract target geometric data. The paper reviews several methods, including regression-based, template-based, and feature-based approaches, as well as refinement methods that improve the initial coarse pose estimation. The paper also discusses the challenges of 6DoF object pose estimation, such as occlusion, lighting variations, and the need for accurate and efficient methods. It highlights the importance of robustness to noise and changes in light, as well as the need for methods that can handle different object shapes and textures. The paper concludes that future research should focus on improving the accuracy and efficiency of 6DoF object pose estimation methods, as well as their applicability to different application scenarios.This paper provides a comprehensive survey of 6DoF object pose estimation methods for different application scenarios. The task involves determining the position and orientation of objects in real-world scenes, which is crucial for applications such as virtual reality, augmented reality, autonomous driving, and robotic operations. The paper categorizes existing methods into instance-level and category-level approaches, based on whether the CAD model of the object is required. It reviews recent advancements in learning-based 6DoF pose estimation methods, exploring their innovations and applicable scenarios. The study provides an overview of widely used datasets, task metrics, and diverse application scenarios. It also compares state-of-the-art methods across publicly accessible datasets, taking into account differences in input data types. The paper summarizes the challenges of current tasks, methods for different applications, and future development directions. The paper discusses various methods for 6DoF object pose estimation, including RGB-based, point cloud or depth-based, and RGB-D-based methods. RGB-based methods use color images to estimate object poses, while point cloud or depth-based methods use depth information or point clouds. RGB-D-based methods combine RGB and depth information to enhance the ability to extract target geometric data. The paper reviews several methods, including regression-based, template-based, and feature-based approaches, as well as refinement methods that improve the initial coarse pose estimation. The paper also discusses the challenges of 6DoF object pose estimation, such as occlusion, lighting variations, and the need for accurate and efficient methods. It highlights the importance of robustness to noise and changes in light, as well as the need for methods that can handle different object shapes and textures. The paper concludes that future research should focus on improving the accuracy and efficiency of 6DoF object pose estimation methods, as well as their applicability to different application scenarios.