# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Real-Time Models  
The model proposed by [PERSON] et al. [4], published in 2015, presents a pose estimation method for rendezvous and docking with passive objects, relevant to space debris removal, by fusing data from a 3D time-of-flight camera and a high-resolution grayscale camera, achieving an average distance error of 3 cm and up to 60 FPS in real-time tests using the European Proximity Operations Simulator (EPOS), making it applicable to space operations. The model BB8 [5], published in 2017, presents a method for 3D object detection and pose estimation using only color images by leveraging segmentation and a CNN-based holistic approach to predict 3D poses, addressing challenges posed by rotationally symmetric objects through restricted training ranges and classification  
The model SSD6D [6], published in 2017, introduces an SSD-based approach for 3D instance detection and 6D pose estimation from RGB images, trained exclusively on synthetic model data, achieving high-speed performance (10 Hz) while demonstrating that color-based methods can effectively replace depth-dependent techniques, with future improvements focusing on robustness to color variations and loss term optimization. The model PoseCNN [7], published in 2017, presents PoseCNN, a convolutional neural network for 6D object pose estimation that separately predicts 3D translation and rotation, introduces loss functions to handle occlusions and object symmetries, and operates using only color images, making it applicable for robotic vision tasks while also contributing the YCB-Video dataset for further research  
The model CDPN [8], published in 2019, presents the Coordinates-based Disentangled Pose Network (CDPN), a 6-DoF object pose estimation method that separately predicts rotation and translation using specialized techniques to enhance accuracy and robustness, making it effective for handling texture-less and occluded objects in real-world applications such as robotics and augmented reality. The model CuIINet [9], published in 2019, proposes CuIINet, a confidence calibration network for single-view object pose estimation that enhances the reliability of pose proposal selection by refining confidence scores using pose masks rendered from 3D models and cropped image regions, with experimental validation on LINEMOD and Occlusion LINEMOD datasets demonstrating its effectiveness. The model Pix2 Pose [10], published in 2019, proposes Pix2 Pose, a 6D object pose estimation method that predicts per-pixel 3D coordinates from RGB images without requiring textured 3D models, using an auto-encoder architecture to estimate 3D coordinates and errors per pixel, generative adversarial training for occlusion recovery, and a transformer loss to handle object symmetries, with validation on multiple benchmark datasets. The model PoseRBPF [11], published in 2019, proposes PoseRBPF, a Rao-Blackwellized particle filter for 6D object pose tracking that decouples 3D translation and 3D rotation by discretizing the rotation space and using an autoencoder-based codebook of feature embeddings, enabling robust tracking of symmetric and textureless objects, with validation on two benchmark datasets for robotic applications such as manipulation and navigation  
The DPOD (Dense Pose Object Detector) model [12], published in 2019, presents DPOD, a deep learning-based method for 3D object detection and 6D pose estimation from RGB images, which computes dense multi-class 2D-3D correspondences to estimate object poses via PnP and RANSAC, followed by a deep learning-based refinement step, demonstrating effectiveness on both synthetic and real training data while being real-time capable.