# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Non-Real-Time Models
[69], published in 2021,introduces a multi-view 6 DoF object pose refinement method that enhances DPOD-based 2D-3D correspondences using a differentiable renderer and geometric constraints, demonstrating robust performance across multiple datasets and enabling automatic annotation of real-world training data for practical applications. The model proposed by [PERSON] et al. [70], published in 2022, presents a self-supervised learning framework for 6 DoF object pose estimation, utilizing depth-basedpose refinement to supervise an RGB-based estimator, achieving competitive accuracy without real image annotations and offering a scalable solution for applications with limited labeled data. The model \"Primitive Pose\" [71], published in 2022,is a 3D pose and size estimation framework for robotic applications, using stereo depth-based geometric cues to predict oriented 3D bounding boxes for unseen objects without CAD models or semantic information, enabling open-ended object recognition in dynamic environments. The model \"RANSAC Voting\" [72], published in 2022, introduces a 6D pose estimation method for robotic grasping, leveraging EfficientNet for feature extraction and per-pixel keypoint prediction using RANSAC voting, demonstrating high accuracy under occlusions and enabling stable object grasping in real-world environments. The model 3 DNEL [73], published in 2023, is a probabilistic inverse graphics framework for robotics and 3D scene understanding, integrating neural embeddings from RGB with depth information to improve robustness in sim-to-real 6D pose estimation, enabling uncertainty quantification, multi-object tracking, and principled scene modeling. The model Multistream ValidNet [74], introduces a validation framework for robotic manipulation that enhances 6D pose estimation by distinguishing between True and False Positive results using depth images and point clouds, improving pose accuracy and robustness in real-world applications. The model C2 FNET [75], published in 2023, is a two-stage 6D pose estimation framework that refines keypoint localization using deformable convolutional networks (DCN), improving accuracy in occlusion-heavy conditions, as demonstrated on LINEMOD and Occlusion LINEMOD datasets. The model DR-Pose [76], published in 2023, is a 6D object pose estimation method that enhances RGBD feature integration using a Deep Fusion Transformer (DFTr) block and improves 3D keypoint localization with a weighted vector-wise voting algorithm,  
The model Improved CDPN [77], published in 2023, presents an enhanced pose estimation method for robotics by employing separate networks for translation and rotation prediction, integrating Convolutional Block Attention Module (CBAM) and Pyramid Pooling Module (PPM) to mitigate feature degradation, and validating its effectiveness in object pose estimation and robotic grasping tasks using the Linemod dataset and real-world experiments. The Improved PVNet 2 model [78], published in 2023, presents an improved PVNet-based pose estimation method with an enhanced ResNet18 backbone and ELU activation function to support real-time pose tracking in virtual-real fusion maintainability test scenarios, validated on a custom Linemod-format air filter dataset for improved accuracy in component detection and virtual reconstruction. The model MSDA [79], published in 2023, introduces a self-supervised domain adaptation approach for 6D object pose estimation that fine-tunes a synthetic pre-trained model using real RGB(-D) images with pose-aware consistency and depth-guided pseudo-labeling, reducing reliance on real pose labels and differentiable rendering while improving real-world applicability. The Scale Adaptive Skip-ASP Pixel Voting Network (SASA-PVNet) model [80], published in 2023, presents SASA-PVNet, a scale-adaptive 6D object pose estimation method that enhances keypoint-based approaches by dynamically resizing small objects and incorporating a Skip-ASP module for multi-scale information fusion,  
The model proposed by [PERSON] et al.