# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 5 Datasets
### Datasets Analytics  
Tables 4-6 and outline the 84 6D pose estimation model analyzed in this study, listing each model alongside its corresponding reference. Presented the 8 most common datasets found to be used in this study. Each model may be evaluated in more than one dataset. Each dataset represented by a specific column: Linemod **(LN)**, OCCLUSION Linemod **(OLN)**, YCB-VIDEO **(YCBV)**, T-LESS **(T-L)**, KITTI **(KIT)**, CAMERA25 **(CAM)**, REAL **(RE)**, and TEJANI **(TEJ)**. An 'X' in the table indicates that a particular model has been validated on the corresponding dataset. Additionally, there are 17 other datasets referenced across the studies that are not included in this table for conciseness but exist in the dataset section. Analytical information about what dataset was used on each model is available in the above section of the model presentation. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Model** & **LN** & **OLN** & **YCBVT-L** & **KIT** & **CAM RE** & **TEJ** \\ \hline [PERSON] et al. [4] & & & & ✓ & & & \\ SSD6D [6] & ✓ & ✓ & & & ✓ & & \\ BB8 [5] & ✓ & ✓ & & ✓ & & & \\ PoseCNN [7] & ✓ & ✓ & ✓ & & & & \\ DeepIM [61] & ✓ & ✓ & & ✓ & & & \\ DPOD [12] & ✓ & ✓ & & & & & \\ Pix2 Pose [10] & ✓ & ✓ & & ✓ & & & \\ [PERSON] et al. [58] & & & & & & & ✓ \\ Compressed YOLO-6D [40] & ✓ & & & & & & \\ CDPN [8] & ✓ & ✓ & & & & & \\ CullNet [9] & ✓ & ✓ & & & & & \\ DeepVCP [59] & & & & & & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Datasets per model (Part I). \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **LN** & **OLN** & **YCBV** & **T-L** & **KIT** & **CAM RE** & **TEJ** \\ \hline PoseRBPF [11] & & & & ✓ & ✓ & & \\ YOLO-6D+ [17] & ✓ & ✓ & & & & & \\ G2L-Net [15] & ✓ & ✓ & ✓ & & & & \\ CosyPose [14] & & & & ✓ & ✓ & & \\ HybridPose [16] & ✓ & ✓ & & & & & \\ EPOS [62] & ✓ & ✓ & ✓ & ✓ & & & \\ AAE [13] & ✓ & & & & ✓ & & \\ Autonomous Mooring Model [60] & & & & & & & \\ LatentFusion [63] & ✓ & & & & & & \\ Self6D [64] & ✓ & ✓ & ✓ & & & & \\ SO-Pose [23] & ✓ & ✓ & ✓ & & & & \\ [PERSON] et al. [68] & & & & & & & \\ ASPP-DF-PVNet [18] & ✓ & ✓ & & & & & \\ BundleTrack [19] & & & & & & & \\ Category Level [65] & & & & & & ✓ & ✓ \\ CloudAAE [20] & ✓ & ✓ & ✓ & & & & \\ FS-Net [21] & ✓ & ✓ & & & & & ✓ \\ Multistream ValidNet [74] & & & & & & & \\ [PERSON] et al. [69] & ✓ & ✓ & ✓ & & & \\ RePOSE [22] & ✓ & ✓ & ✓ & & & \\ [PERSON] et al. [24] & & & & & & & \\ FFB6D [26] & ✓ & ✓ & ✓ & & & \\ DPOPV2 [27] & ✓ & ✓ & & & ✓ & \\ ConvPoseCNN2 [66] & & & & & ✓ & & \\ [PERSON] et al. [81] & & & ✓ & ✓ & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Datasets per model (Part II). The statistical analysis presented in Figure 2 reveals that the majority of the models have been validated on the OCCLUSION Linemod and Linemod datasets, with 24.88% and 23.96% of the models respectively utilizing these benchmarks. The YCB-VIDEO \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **LN** & **OLN** & **YCBVT-L** & **KIT** & **CAM** & **RE** & **TEj** \\ \hline CoS-PVNet [51] & ✓ & ✓ & & & & & & \\ Focal Segmentation [53] & ✓ & ✓ & & & & & & \\ [PERSON] et al. [85] & & & & & & & & \\ Lite-HRPE [55] & ✓ & & & & & & & \\ BSAM-PVNet [50] & ✓ & ✓ & & & & & & \\ RNNPose [86] & ✓ & ✓ & ✓ & ✓ & & & \\ YOLOX-6D-Pose [56] & & & & ✓ & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Datasets per model (Part III).