# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Non-Real-Time Models  
The model \"[PERSON] et al.\" [58], published in 2019, introduces a 3D object detection and pose estimation framework for robotic applications, combining SSD-based object detection, modified LineMOD template matching, and ICP refinement to improve accuracy and reduce false positives, demonstrating its effectiveness in real-world robotic grasping and polishing tasks. The model DeepVCP [59], published in 2019, presents an end-to-end 3D hand pose estimation framework that eliminates the need for prior hand information by introducing a keypoint-based detection method and integrating biological hand constraints, improving accuracy and robustness for real-world applications in unconstrained environments. The \"Autonomous Mooring\" model [60], published in 2020, proposes an algorithm for autonomous mooring that refines billard detection by converting Mask R-CNN segmentation into a single reference point, improving localization accuracy and reducing error for precise maritime navigation and offshore operations. The model DeepIM [61], published in 2018, is a deep learning-based 6D pose estimation framework for robot manipulation and virtual reality, using an iterative pose refinement approach that predicts relative transformations from color images, enabling accurate pose estimation without depth data or prior object models. The model EPOS [62], published in 2020, introduces a 6D object pose estimation method that models objects using compact surface fragments, predicts per-pixel 3D correspondences, and refines poses with a robust PnP-RANSAC algorithm, enabling accurate pose estimation for diverse objects, including those with global or partial symmetries. The model LatentFusion [63], published in 2020, presents a keypoint-based 6D pose estimation framework for spacecraft applications, using a two-stage neural network to predict keypoints and infer pose, achieving real-time processing while balancing accuracy and computational efficiency for deployment on space-grade hardware. The model Self6D [64], published in 2020, introduces Self6D, a self-supervised 6D pose estimation framework that refines models trained on synthetic data using neural rendering and geometric constraints, enabling improved accuracy on real-world data without requiring 6D pose annotations. The model \"Category Level Metric Scale Object Shape and Pose Estimation\" [65], published in 2021, introduces a framework for estimating metric scale shape and 6D pose from a single RGB image, utilizing the MSOS and NOCS branches along with the NOCE module to predict object mesh, coordinate space, and geometrically aligned object centers, demonstrating strong performance in robotics and augmented reality applications. The model ConvPoseCNN2 [66], published in 2021, is a fully convolutional 6D object pose estimation framework for robotics, utilizing dense pixel-wise predictions to improve spatial resolution and inference speed while integrating an iterative refinement module to enhance accuracy, making it well-suited for cluttered environments. The model DCL-Net [67], published in 2022, is a deep learning-based 6D object pose estimation framework that enhances correspondence learning between partial object observations and complete CAD models using dual Feature Disengagement and Alignment modules, integrating confidence-weighted pose regression and iterative refinement for improved accuracy across multiple benchmark datasets. The \"[PERSON] et al.\" model [68] using Augmented Auto Encoder and Faster R-CNN, published in 2021, introduces a joint learning framework that integrates object detection and pose estimation, using shared feature maps to enhance pose accuracy by reducing errors from mislocalized bounding boxes, with potential applications in real-world object recognition tasks. The model proposed by [PERSON] et al.