# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Real-Time Models
The model SC6D [33], published in 2022, presents SC6D, a symmetry-agnostic, correspondence-free 6D object pose estimation framework that utilizes an SO(3) encoder for rotation learning, object-centric coordinate transformations for localization, and classification-based depth estimation, eliminating the need for CAD models on the T-LESS dataset. The model SSP-Pose [34], published in 2022, presents SSP-Pose, an end-to-end category-level 6D pose estimation framework that integrates shape priors into direct pose regression, leveraging symmetry-aware constraints and multiple learning branches to improve accuracy while maintaining real-time inference speed, with applications in robotics, autonomous driving, and augmented reality. The model proposed by [PERSON] et al. [35], published in 2022, presents a multi-scale convolutional feature fusion framework for 6D object pose estimation, enhancing correspondence-based keypoint extraction through residual learning and improved feature representation, achieving higher accuracy and robustness in challenging conditions, with applications in robotic grasping and real-time object tracking. The model proposed by [PERSON] et al. [36], published in 2022, presents a PVNet-based 6D object pose estimation framework for robotic arm applications in smart manufacturing, leveraging multi-angle image transformations and point cloud registration to improve pose estimation, feature extraction, and object grasping accuracy, addressing limitations of traditional 2D vision-based systems. The model ZebraPose [37], published in 2022, presents a coarse-to-fine surface encoding technique for 6D object pose estimation, introducing a hierarchical binary grouping strategy for efficient 2D-3D correspondence prediction, leveraging a PnP solver for final pose estimation, achieving state-of-the-art accuracy on benchmark datasets, with applications in robotics, augmented reality, and industrial automation. The model article [38], published in 2022, introduces Gen6D, a generalizable model-free 6D pose estimator that predicts object poses using only posed images, integrating a novel viewpoint selector and volume-based pose refiner to achieve accurate results for unseen objects in arbitrary environments. The model proposed by [PERSON] et al. [39], published in 2023, presents a two-stage deep learning framework for spacecraft 6D pose estimation, where a convolutional network predicts keypoint locations, and a Pose Inference Network estimates the final pose,achieving efficient processing for space-grade hardware while maintaining competitive accuracy on the SPEED dataset. The model Compressed YOLO-6D [40], published in 2023, presents a mobile-optimized real-time 6D pose estimation framework for augmented reality (AR), enhancing YOLO-6D with model compression techniques such as channel pruning and knowledge distillation to improve inference speed and efficiency, enabling low-latency AR interactions on mobile devices. The CRT-6D model [41], published in 2022, presents CRT-6D (Cascaded Pose Refinement Transformers), a real-time 6D object pose estimation framework that replaces dense intermediate representations with Object Surface Keypoint Features (OSKFs) and employs deformable transformers for iterative pose refinement, achieving state-of-the-art performance on benchmark datasets while being significantly faster than existing methods, with applications in robotics, augmented reality, and industrial automation. The model DFIT network [42], published in 2023, presents DR-Pose, a two-stage 6D pose and 3D size estimation framework that integrates point cloud completion for unseen part recovery and scaled registration for pose-sensitive feature extraction, with applications in robotics, augmented reality, and automated object recognition.