# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 5 Datasets
### Datasets Summaries
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Dataset Name** & **Type** & **Number of Images** & **Characteristics** \\ \hline
[94] ModelNet & Synthetic & 151,128 3D CAD models across 660 categories & Addresses the limitations of earlier CAD datasets by providing a large and diverse set of models \\ \hline
[63] MOPED & Real & Approximately 300 test images per  object for 11 objects & Model-free object pose estimation \\ \hline
[95] MP6D & Both & 77 video sequences, 20,100 real-world  RGB-D frames, 50,000 synthetic  images & Industrial environment metal parts made from aluminum and copper alloys, characterized by textureless surfaces, symmetrical shapes, and high reflectivity \\ \hline
[30] MV-YCB FixCam, WiggleCam, and MovingCam & Synthetic & Not specified & 6D pose estimation for cluttered scenes with non-symmetric objects from the YCB set \\ \hline
[96] NOCS & Both & Main dataset apart from the Real and CAMERA dataset \\ \hline
[97] ShapeNetCore & Synthetic & 51,300 3D models across 55 object categories & A richly annotated subset of the ShapeNet repository, focusing on providing single, clean 3D models with verified category and alignment annotations \\ \hline
[98] Spacecraft Pose Estimation & Both & 12,000 synthetic images, 300 real  images & 6D object pose estimation for satellite pose estimation, images and poses of the Tango spacecraft from the PRISMA mission \\ \hline
[99] T-LESS & Real & 39,000 training images, 10,000 test  images & 6D pose estimation of texture-less, rigid objects in cluttered and occluded scenes, also for industrial parts \\ \hline
[100] Tejani & Real & Not specified & 3D object detection and 6D pose estimation in cluttered and occluded scenes, multiple sequences of tabletop scenes featuring varying levels of occlusion and clutter \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summary of Datasets for 6D Pose Estimation (Part II). \begin{table}
\begin{tabular}{l c c} \hline \hline
**Dataset Name** & **Type** & **Number of Images** & **Characteristics** \\ \hline
[24] Tangong-1 & Synthetic & 15,000 rendered images paired with depth  maps & 6D pose estimation of spacecraft, specifically the Tiangong-1 satellite, satellite image rendering pipeline that accounts for atmospheric turbulence and other imaging distortions for training and evaluating neural networks for 6D pose estimation of spacecraft \\ \hline
[32] Truncated Linemod & Synthetic & Not specified & Assessing robustness of 6D pose estimation under object truncation \\ \hline
[101] TUD-L & Real & Not specified & 6D object pose estimation in cluttered and occluded real-world environments \\ \hline
[102] YCBInEOAT & Real & 9 video sequences capturing different object  manipulation tasks & 6D pose tracking in dynamic robotic manipulation scenarios, where objects remain static while the camera moves \\ \hline
[7] YCB-Video & Real & 92 video sequences, 133,827 frames with 2,949 keyframes for evaluation & 6D object pose estimation in cluttered and occluded scenes using YCB object set \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of Datasets for 6D Pose Estimation (Part III).