# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Real-Time Models
The model AAE [13], published in 2018, presents a real-time RGB-based pipeline for object detection and 6D pose estimation, utilizing an Augmented Autoencoder trained on synthetic 3D model views with domain randomization to enable robust self-supervised 3D orientation estimation across various RGB sensors, without requiring pose-annotated training data, while inherently handling object symmetries and perspective errors. The model CosyPose [14], published in 2020, presents CosyPose, a multi-view 6D object pose estimation method that integrates single-view predictions, robust hypothesis matching, and object-level bundle adjustment for global scene refinement, using a render-and-compare approach to handle unknown camera viewpoints, explicitly managing object symmetries, and operating without depth measurements, with applications in visually driven robotic manipulation. The model G2L-Net [15], published in 2020, presents G2L-Net, a real-time 6D object pose estimation framework that processes point clouds from RGB-D detection using a divide-and-conquer approach, incorporating 3D sphere-based localization to constrain the search space, embedding vector features (EVF) for viewpoint-aware rotation estimation, and a rotation residual estimator for refinement  
The model HybridPose [16], published in 2020, presents HybridPose, a real-time 6D object pose estimation method that integrates keypoints, edge vectors, and symmetry correspondences as a hybrid intermediate representation, enhancing robustness to occlusions and extreme poses while filtering outliers through a robust regression module, with future extensions planned for incorporating additional geometric features. The model YOLO-6D+ [17], published in 2020, presents YOLO-6D+, an end-to-end deep learning framework for real-time 6D object pose estimation from a single RGB image, incorporating a silhouette prediction branch to enhance feature learning and an edge restrain loss to improve 3D shape constraints, predicting 2D keypoints for PnP-based pose estimation, demonstrating efficiency for augmented reality and robotic grasping applications. The model ASPP-DF-PVNet [18], presents ASPP-DF-PVNet, an occlusion-resistant 6D pose estimation framework that enhances PVNet with an Atrous Spatial Pyramid Pooling (ASPP) module for improved segmentation and a distance-filtered voting scheme for better keypoint localization, demonstrating effectiveness on LINEMOD and Occlusion LINEMOD datasets. The model BundleTrack [19], published in 2021, presents BundleTrack, a real-time 6D pose tracking framework that operates without relying on instance- or category-level 3D models, combining deep learning-based segmentation, robust feature extraction, and pose graph optimization for long-term, low-drift tracking under challenging conditions, with applications in robotic manipulation, pick-and-place, and in-hand dexterous tasks. The model CloudAAE [20], published in 2021, presents a point cloud-based 6D pose estimation system that leverages an augmented autoencoder (AAE) for pose regression and a lightweight synthetic data generation pipeline, significantly reducing training costs while enabling agile deployment for robotic applications, demonstrating effectiveness among synthetic-trained methods on public benchmarks. The model FS-Net [21], published in 2021, presents FS-Net, a real-time category-level 6D pose estimation framework that utilizes a 3D graph convolutional autoencoder for feature extraction, a decoupled rotation mechanism for improved orientation decoding, and a residual-based translation and size estimation strategy, demonstrating strong generalization and efficiency for fast and accurate object pose tracking. The model RePOSE [22], published in 2021,a real-time 6D object pose refinement method that replaces CNN-based refinement with efficient deep texture rendering and differentiable Levenberg-Marquardt optimization, enabling fast and accurate pose tracking, with potential applications in real-time object manipulation and robotics.