# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 4 Models
### Real-Time Models
The SO-Pose model [23], published on 2021, presents SO-Pose, an end-to-end 6D pose estimation framework that introduces a two-layer model combining 2D-3D correspondences and self-occlusion reasoning, enhancing spatial reasoning and robustness in cluttered environments, with potential applications in object tracking, manipulation, and self-supervised pose estimation. The model developed by [PERSON] et al. [24], published in 2021, presents a satellite pose estimation framework that leverages a degraded image rendering pipeline to simulate atmospheric turbulence and a deep learning-based method inspired by YOLO-6D, incorporating a relative depth prediction branch to enhance pose estimation accuracy, with applications in ground-based optical telescope tracking of non-cooperative space targets. The model GDR-NET [25], published in 2021,presents GDR-Net, a geometry-guided direct regression network for end-to-end 6D object pose estimation, integrating dense geometric feature representations with a Patch-PnP module to enable real-time, differentiable, and accurate pose estimation, with applications in robotics, augmented reality, and computer vision tasks requiring differentiable poses. This paper [26], published in 2021 introduces FFB6D, a Full Flow Bidirectional fusion network for 6D pose estimation that integrates RGB and depth features throughout the encoding and decoding process and incorporates a SIFT-FPS keypoint selection algorithm, demonstrating strong performance on benchmark datasets with potential applications in 3D perception tasks. The model DPOPV2 [27], published in 2022 presents DPODv2, a three-stage 6D object pose estimation framework that integrates YOLOv3-based detection, CENet for dense 2D-3D correspondences, and multi-view optimization, supporting both RGB and depth modalities. It also introduces a differentiable rendering-based refinement stage to improve pose consistency across multiple views, demonstrating scalability and strong performance across multiple datasets. The model FS6D-DPM [28], published in 2022, introduces FS6D-DPM, a few-shot 6D object pose estimation framework that predicts the pose of unseen objects using only a few support views, leveraging dense RGBD prototype matching with transformers, ShapeNet6D for large-scale pre-training, and online texture blending for enhanced generalization, addressing challenges in open-set pose estimation with potential applications in robotics and augmented reality. The model GPV-Pose [29], published in 2022,presents GPV-Pose, a category-level 6D pose estimation framework that introduces a confidence-driven rotation representation and a geometry-guided point-wise voting mechanism to improve robustness across intra-class variations, achieving real-time inference speed and state-of-the-art performance on public datasets, with applications in robotics, augmented reality, and 3D scene understanding. The model MV6D [30], published in 2022, presents MV6D, an end-to-end multi-view 6D pose estimation framework that fuses RGB-D data from multiple perspectives using DenseFusion and joint point cloud processing, achieving robust pose estimation in cluttered and occluded scenes, with applications in robotics, augmented reality, and autonomous systems. The model OVE6D [31], published in 2022, presents OVE6D, a model-based 6D pose estimation framework that decomposes pose into viewpoint, in-plane rotation, and translation, using lightweight cascaded modules trained purely on synthetic data, enabling strong generalization to real-world objects without fine-tuning, with applications in robotics, industrial automation, and large-scale object recognition. The model PVNet (Pixel-wise Voting Network) [32], published in 2022, presents PVNet, a 6D object pose estimation framework that uses a pixel-wise voting mechanism for keypoint localization and an uncertainty-driven PnP solver for final pose estimation, improving robustness against occlusion and truncation, with applications in robotics, augmented reality, and automated inspection.