# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 6 Analysis
### Models by Input Type  
In this section, we categorize the 84 analyzed 6D pose estimation models based on their input types, distinguishing between single image and non-single image models. Tables 14-17 present the detailed specifications of these models, including their processing speeds measured in frames per second (FPS) and the types of input data they utilize. Out of the total models (84), 71 are single image models, while the remaining 13 are non-single image models. Among the single image models, 14 operate below 13 FPS, 11 up 25 FPS and 48 achieve speeds exceeding 25 FPS. However, speed information is unavailable for 13 of these models. In contrast, within the non-single image category (13 models), 3 models run below 10 FPS, 4 models are able to run between 20 adn 50 FPs and 6 models lack speed information. Analyzing the input types, a majority of the models accept RGB images, with 56 models supporting this input modality 52 of which are single image models and 4 are multi-image models. From the 52 Single image models 38 accept only one RGB image while 14 accept more than one modality. RGB-D inputs are supported by 21 models, comprising 17 single image and 4 multi-image models. Of the 21 single image models 17 accept only RGB-D images and 4 accept also other modalities. Additionally, 7 models utilize 3D data inputs (6 single and 1 multi), while 10 models incorporate Lidar and Point Cloud data (5 single and 5 multi). Depth Maps are accepted by 8 models (6 single and 2 multi), and specialized inputs such as Pose Masks and Gray Scale images are supported by 2 single image models each. This distribution indicates a strong preference for RGB-based inputs in both single and multi-image models, reflecting their versatility and widespread applicability in various pose estimation tasks. The presence of models utilizing RGB-D, 3D, Lidar, Point Cloud, Depth Map, Pose Mask, and Gray Scale inputs underscores the diversity of approaches in the field, catering to different application requirements and environmental conditions. Moreover, the higher prevalence of single image models with RGB inputs suggests that these models strike a balance between performance and computational efficiency, making them suitable for real-time applications where speed is paramount. Figure 3: Model Distribution Based on Model Speed. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model Name** &
\begin{tabular}{c} **Speed** \\ **(FPS)** \\ \end{tabular} & **RGB** & **RGB-D** & **3D** & **Lidar** & **Depth** & **Pose** & **Gray** \\ \hline Category Level Metric Scale Object & & ✓ & & & & & \\ Shape and Pose Estimation [65] & & & & & & & \\ ConvPoseCNNN2 [66] & & ✓ & & & & & \\ [PERSON] et al. [35] & & ✓ & & & & & \\ C2 FNET [75] & & ✓ & & & & & \\ Improved PVNet [45] & & ✓ & & & & & \\ SASA-PVNet [80] & & ✓ & & & & & \\ BSAM-PVNet [50] & & ✓ & & & & & \\ EPOS [62] & 1.3 & ✓ & ✓ & & & & \\ GEN6D [38] & 1.56 & ✓ & & & & ✓ \\ ZebraPose [37] & 9.1 & ✓ & ✓ & & & & \\ IFFNeRF [57] & 34 & ✓ & ✓ & & & & \\ AAE [13] & 42 & ✓ & ✓ & & & & \\ FEB6D [26] & 13.33 & ✓ & & & & & ✓ \\ GDR-NET [25] & 45.5 & ✓ & ✓ & & & & \\ PoseCNN [7] & & ✓ & & & ✓ & & \\ [PERSON] et al. [85] & & & & & ✓ & & \\ LatentFusion [63] & & & & & ✓ & & \\ [PERSON] et al. [48] & 6.7 & ✓ & & & & ✓ & \\ EPro-PnP [52] & 31 & ✓ & ✓ & & & & \\ Autonomous Mooring Model [60] & & & & ✓ & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Single Image Models and Input Types (Part II).