# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Real-Time Models
The model PoET [43], published in 2022, presents PoET, a transformer-based 6D multi-object pose estimation framework that predicts object poses using only a single RGB image, integrating object detection with a transformer decoder to process multiple objects simultaneously, with applications in robotic grasping, localization, and real-time perception in resource-constrained environments. The model HS-Pose [44], published in 2023, presents HS-Pose, a category-level 6D object pose estimation framework that introduces the HS-layer, an improved 3D graph convolutional module designed to enhance local-global feature extraction, robustness to noise, and encoding of size and translation information, running in real-time, with applications in robotics, augmented reality, and industrial automation. The Improved PVNet model [45], published in 2023, presents an enhanced PVNet-based 6D pose estimation framework for amphibious robots, introducing confidence score prediction and keypoint filtering to improve accuracy in occluded target scenarios, with applications in robotic tracking, docking, and grasping, and plans for further optimization to reduce computational costs and improve real-time performance. The 6D object pose estimation model proposed by [PERSON] et al. [46], published in 2023, presents an enhanced voting-based 6D object pose estimation method that improves PVNet by introducing a distance-aware vector-field prediction loss and a vector screening strategy, reducing angular deviations in keypoint predictions and preventing parallel vector hypotheses, achieving higher accuracy on benchmark datasets, with future work aimed at applying these improvements to robotic grasping tasks. The model PLuM [47], published in 2023, presents PLuM (Pose Lookup Method), a probabilistic reward-based 6D pose estimation framework that replaces complex geometric operations with precomputed lookup tables, enabling accurate and efficient pose estimation from point clouds, with applications in field robotics, including real-time haul truck tracking in excavation scenarios. The model proposed by by [PERSON] et al. [48], published in 2023, presents a real-time 6D pose estimation framework for in-hand suture needle localization, incorporating a novel state space and feasible grasping constraints into Bayesian filters, ensuring consistent and accurate needle tracking relative to the end-effector, with applications in autonomous suturing and surgical robotics. The model SE-UF-PVNet [49], published in 2023, is a 6 DoF object pose estimation framework that enhances keypoint localization from a single RGB image by integratingstructural information via a keypoint graph and Graph Convolution Network, utilizing novel vector field predictions and multi-scale feature extraction to improve robustness, particularly in occlusion scenarios, while maintaining real-time inference. The model BSAM-PVNet [50], published in 2024, introduces BSAM-PVNet, a two-stage pose estimation method combining ResNet18 with blueprint separable convolutions and a convolutional attention mechanism for feature extraction, validated on a self-built insulator dataset, highlighting improvements in accuracy and efficiency while addressing generalization challenges in future research. The model CoS-PVNet [51], published in 2024, is a robust 6D pose estimation framework designed for complex environments in augmented reality, virtual reality, robotics, and autonomous driving, enhancing PVNet with pixel-weighting, dilated convolutions, and a global attention mechanism to improve keypoint localization and feature extraction, demonstrating strong performance in occlusion-heavy conditions and potential for broader industry applications.