# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 1 Introduction  
Six Degrees of Freedom (6D) pose estimation is a pivotal task in the realm of computer vision, aimed at accurately determining the position and orientation of objects within a three-dimensional space. This capability is essential for a multitude of applications, including robotics, augmented reality (AR), virtual reality (VR), autonomous driving, industrial automation, and satellite navigation. In robotics, precise 6D pose estimation enables manipulators to interact seamlessly with objects, facilitating tasks such as assembly, sorting, and handling. With the evolution of technology, robotic items now exist even inside our homes like robotic vacuum cleaners and the importance of fast, accurate, and reliable object position detection will be even more crucial. In AR and VR, understanding the spatial orientation of objects enhances the realism and interactivity of virtual environments, and the user experience is achieved by the ability of the object detection models to place virtual objects in a real environment. Autonomous vehicles rely on 6D pose estimation for obstacle detection, navigation, and environment mapping, ensuring safe and efficient operation. Moreover, in industrial settings, 6D pose estimation supports automated quality control, inventory management, and complex manufacturing processes by providing reliable object localization and tracking. Despite significant advancements, 6D pose estimation remains a challenging problem due to factors such as occlusions, varying lighting conditions, object symmetries, and the necessityfor real-time performance on small devices. The diversity of input modalities--ranging from RGB images to RGB-D, 3D models, LiDAR, and depth maps--further complicates the development of robust and versatile pose estimation models. As the demand for accurate and efficient 6D pose estimation grows, along with new publications, there is a need for comprehensive reviews that present the new findings and provide detailed comparisons based on performance metrics, input types, and application-specific requirements. The main research question of this study is to create a detailed selection guide for 6D pose estimation object models, addressing the primary characteristics that are critical when selecting a model for a specific application. These characteristics include input modalities, real-time capabilities, hardware requirements, datasets used for evaluation, performance metrics, objects tested on, strengths, limitations, and special attributes such as symmetry and occlusion handling. To answer this question, we systematically analyzed 83 computer vision models, generating template summaries for each model, categorizing the models and developing comparison tables to facilitate informed decision-making. This study offers several novel contributions to the field of 6D pose estimation:  
1. **Comprehensive Model Analysis**: We systematically analyzed 83 computer vision models for 6D pose estimation (61 new from the latest review), addressing key characteristics such as input types, real-time capabilities, hardware requirements, datasets used, performance metrics, strengths, limitations, and the ability to handle complex scenarios like symmetrical objects and occlusions. 2. **Expanded Dataset Coverage**: In addition to identifying and describing 25 distinct datasets(14 new from the latest review) providing detailed summaries and statistical insights into their adoption within the research community. 3. **Analytical Specification Tables**: We introduced detailed analytical tables that summarize each model's characteristics, performance metrics, and capabilities. These tables serve as a novel resource for quick comparisons and informed selection, offering a structured overview that was previously unavailable. 4. **Categorization of Models**: To enhance organization and usability, models are categorized into **Real-Time Models** and **Non-Real-Time Models**.