# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 6 Analysis
### Metric Evaluation
#### 6.1.1 Linemod Dataset  
Tables 7 and 8 present the evaluation metrics for various 6D pose estimation models on the Linemod dataset. Analysis of these tables reveals that certain models consistently achieve the highest values across different metrics. Notably, the **DFTr network**[42] attains the highest scores in both the **ADD** and **AUC ADD** metrics with values of 99.80%. For the **ADD-S** metric, which accounts for symmetric objects, **GPV-Pose**[29] leads with a score of 98.20%, while **DCL-Net**[67] achieves the highest value of 99.50% in the **ADD(-S)** metric. Regarding the **Average Recall (AR)** metric, **FS-Net**[21] records the top performance with a score of 97.60%. In the **AUC ADD(-S)** metric, **FS-Net**[21] also achieves the highest value of 95.92%.  
Figure 2: Distribution of Dataset Usage Across 6D Pose Estimation Models.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **Year** & **ADD** & **ADD-S** & **ADD(-S)** & **AR** & **AUC ADD** & **AUC ADD(-S)** \\ \hline SSD6D [6] & 2017 & 90.90 & & & & & \\ BB8 [5] & 2017 & & & & & 62.70 & \\ PoseCNN [7] & 2017 & & & & & 67.67 & \\ DeepIM [61] & 2018 & & & & 98.70 & & \\ DPOD [12] & 2019 & 95.15 & & & & \\ Pix2 Pose [10] & 2019 & & & & 0.98 & \\ Compressed YOLO-6D [40] & 2019 & 54.52 & & & & \\ CDPN [8] & 2019 & 75.04 & 13.00 & & & \\ CullNet [9] & 2019 & & 78.30 & & & \\ YOLO-6D+ [17] & 2020 & & 67.67 & & & \\ G2L-Net [15] & 2020 & & 98.70 & & & \\ HybridPose [16] & 2020 & & 94.50 & & & \\ EPOS [62] & 2020 & & & & & \\ AAE [13] & 2020 & 81.00 & & & 81.00 & \\ LatentFusion [63] & 2020 & 94.70 & & 94.70 & & 22.00 \\ Self6D [64] & 2020 & & & 28.00 & & \\ SO-Pose [23] & 2021 & & 45.90 & & & \\ ASPP-DF-PVNet [18] & 2021 & & 91.18 & & & \\ CloudAAE [20] & 2021 & 95.50 & 66.10 & & & \\ FS-Net [21] & 2021 & & 97.60 & & 97.60 & \\ [PERSON] et al. [69] & 2021 & 74.00 & & & 74.00 & \\ RePOSE [22] & 2021 & & 96.10 & & & \\ FFB6D [22] & 2021 & 99.7 & & & & \\ DPCPV2 [27] & 2022 & 93.59 & & 70.00 & & \\ GDR-NET [25] & 2022 & & & 71.00 & & \\ DCL-Net [67] & 2022 & & & 99.50 & & \\ EPro-PnP [52] & 2022 & & & 67.36 & & \\ RANSAC Voting [72] & 2022 & 93.60 & & & & \\ FS6D-DPM [28] & 2022 & 83.40 & & & & \\ GPV-Pose [29] & 2022 & & 98.20 & & & \\ [PERSON] et al. [35] & 2022 & & & 96.22 & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: Model Evaluation (in %) on Linemod Dataset (Part I).  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **Year** & **ADD** & **ADD-S** & **ADD(-S)** & **AR** & **AUC ADD** & **AUC ADD(-S)** \\ \hline OVE6D [31] & 2022 & & & 0.99 & & 0.99 \\ [PERSON] et al. [70] & 2022 & 88.40 & & & 88.40 & \\ Gen6D [38] & 2022 & 94.11 & & & & \\ Improved CDPN [77] & 2023 & 92.62 & & 95.75 & & 92.62 \\ DFTr network [42] & 2023 & 99.80 & & & 99.80 & \\ SwinDePose [83] & 2023 & & 96.30 & & & \\ C2 FNET [75] & 2023 & & 95.92 & & & \\ SE-UF-PVNet [49] & 2023 & & & 7.00 & 26.00 & 26.00 \\ Improved PVNet [45] & 2023 & 79.94 & & & & \\ [PERSON] et al. [54] & 2024 & & & 88.02 & & \\ CoS-PVNet [51] & 2024 & & & 95.40 & & 95.40 & \\ Focal Segmentation [53] & 2024 & 80.42 & & & 80.42 & \\ Lite-HRPE [55] & 2024 & & 87.57 & & & \\ BSAM-PVNet [50] & 2024 & & 94.20 & & & \\ RNNPose [86] & 2024 & & 85.67 & & & \\ \hline \hline \end{tabular}
\end{table}
Table 8: Model Evaluation (in %) on Linemod Dataset (Part II).  
metric, **HybridPose**[16] leads with 79.2%, and **OVE6D**[31] records the highest **ADD(-S)** value of 74.80%. In terms of **Average Recall (AR)**, both **DeepIM**[61] and **Pix2 Pose**[10] achieve the highest scores of 93%. Additionally, **Pix2 Pose**[10] stands out with an **AUC ADD** score of 93%, while **DeepIM**[61] leads the **AUC ADD(-S)** metric with 95.92%.