# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 7 Discussion  
This study provides a comprehensive analysis of 84 computer vision models developed for 6D pose estimation, examining a diverse range of attributes and performance aspects. By systematically addressing nine key questions for each model--spanning from publication year and input modality to real-time capabilities, dataset usage, evaluation metrics, tested objects, strengths and limitations, and the capacity to handle various challenging conditions--we have established a multifaceted guide in order to help select the appropriate model by execution task and needs. Our findings reveal a rich ecosystem of datasets employed in the literature, with a total of 29 distinct datasets identified. Among these, eight stand out as the most frequently utilized--Linemod, Occlusion Linemod, YCB-Video, T-LESS, KITTI, CAMERA25, REAL25, and Tejani--serving as common ground for benchmarking and comparison. This reliance on a limited set of widely accepted datasets ensures a degree of standardization but may inadvertently limit broader exploration. Expanding the diversity of benchmark datasets could help models generalize more effectively to heterogeneous real-world conditions. The assessment of performance metrics focused on six core measures (ADD, ADD-S, ADD(-S), AR, AUC ADD, and AUC ADD(-S)) underscores the need for common evaluation standards. Models demonstrating top performance on these metrics often excel in handling symmetrical objects and occlusions, key challenges inherent in many real-world scenarios. However, while performance on established benchmarks is strong, additional metrics and more varied evaluations are necessary to fully capture a model's robustness and versatility, particularly for less-studied challenges. In many models, these metrics were available but not in an amount such that we could generalize results. In terms of real-time capabilities, we noted a wide range of speeds and GPU dependencies. Some models achieve impressive frame rates suitable for demanding applications like robotics or augmented reality, and we show that low-cost mid-level GPUs could handle these models' executions. On the other hand, many models are found to need the latest state-of-the-art cloud-based GPUs for training. Striking a balance between accuracy and efficiency remains an ongoing challenge, as reducing computational overhead without sacrificing performance is critical for broader adoption, especially in resource-constrained environments. An examination of the input modalities confirms the predominance of RGB-based methods, likely due to their general availability and ease of integration. Nevertheless, the presence of models leveraging RGB-D, 3D, LiDAR, point cloud, and depth map inputs indicates a growing interest in leveraging richer data modalities for improved accuracy and robustness. Single-image approaches tend to offer higher speeds, while multi-image or multi-modal methods, although generally slower, may yield more robust estimates under complex conditions. Classifying model capabilities with respect to symmetries, multiple objects, identical object instances, transparency, shape variability, and occlusions reveals both strengths and gaps. From all models 10 (11.9%) provided no info about each capability. 65.48% (55 models) \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Model** & **D\&MD** & **LG\&R** & **CC\&EC** & **NES\&LB** & **A\&MR** \\ \hline [PERSON] et al. [85] & ✓ & ✓ & & ✓ & & \\ Lite-HPPE [55] & & & & ✓ & ✓ & \\ BSAM-PVNet [50] & ✓ & ✓ & & & & \\ RNNPose [86] & & ✓ & ✓ & & & ✓ \\ YOLOX-6D-Pose [56] & ✓ & ✓ & ✓ & & & ✓ \\ IFFNeRF [56] & ✓ & & & & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 23: _Cont._can handle symmetrical objects, 44.5% (37 models) can handle many objects on the same scene, 57.14% (48 models) can address occlusions, 10.71% (9 models) can manage multiple instances of the same object, and a mere 2.38% (2 models) can cope with transparency. These shortcomings highlight the complexities that remain unaddressed, signaling important avenues for future research.