# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 9 Limitations  
While this study provides a comprehensive overview of 6D pose estimation models, several limitations must be acknowledged to contextualize the findings and guide future research efforts:  
1. **Database Restriction**: The literature review was exclusively conducted using the Scopus database. Although Scopus is a robust and extensive repository of scientific publications, relying solely on a single database may result in the exclusion of relevant studies published in journals or conferences not indexed by Scopus. Consequently, some pertinent models and advancements in the field might have been overlooked, potentially limiting the comprehensiveness of the review.
2. **Initial Focus on RGB Models**: The research initially concentrated on RGB-based 6D pose estimation models, as outlined in the foundational survey by [PERSON] et al. [3]. This primary focus was later expanded to include additional input modalities such as RGB-D, 3D models, LiDAR, and depth maps based on the information gathered during the analysis of the selected articles. However, this approach may have inadvertently neglected other emerging modalities or articles presenting models that primarily utilize different types of input data. As a result, there may exist relevant models employing alternative modalities that were not captured within the scope of this study.
3. **Standardization of Evaluation Metrics**: The evaluation metrics reported across different models varied significantly in their presentation and emphasis. To facilitate meaningful comparisons, this study standardized the focus to six common metrics: ADD, ADD-S, ADD-(S), average recall (AR), AUC ADD, and AUC ADD-(S). While this approach ensures a consistent benchmark for comparison, it inherently excludes other valuable metrics that different studies might have employed to assess model performance. This standardization may therefore limit the ability to fully capture the multifaceted performance characteristics of each model.
4. **Focus on Evaluation Characteristics Over Internal Mechanisms**: The primary objective of this review was to catalog and compare models based on their evaluation metrics, input types, real-time capabilities, and other selection characteristics. Consequently, the internal workings, architectural designs, and algorithmic innovations of the models were not extensively analyzed. This focus means that nuanced insights into the methodological advancements and unique approaches that differentiate each model are not comprehensively covered, potentially overlooking key factors that contribute to their performance and applicability.
5. **Exclusion of Application Articles and Reviews**: This study included only articles that present a 6D pose estimation model, thereby excluding application-focused studies and review articles. As a result, insights derived from practical implementations and meta-analyses of existing models were not considered, potentially limiting the breadth of perspectives on the application and comparative effectiveness of different pose estimation approaches.  
These limitations highlight areas where future research could expand to provide a more exhaustive and nuanced understanding of 6D pose estimation models. Expanding the literature search to include multiple databases, incorporating a broader range of input modalities, standardizing a wider array of evaluation metrics, delving deeper into the inter nal mechanisms of models, and including application-focused studies and reviews would enhance the robustness and comprehensiveness of subsequent reviews in this domain.