# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 6 Analysis
### Real-time Capabilities  
In this section, we categorize the models based on their ability to operate in real-time environments. This distinction is essential for researchers who aim to implement pose estimation in applications where processing speed and hardware constraints are critical. The results are presented in two separate tables, Part I (Table 12) and Part II (Table 13). Each includes their processing speed measured frames per second (FPS), along with the GPU hardware required for their execution, and a column that shows the one marked as real-time by the authors. In order for a more accurate classification we sort the models per speed so it can be more effective to select a model based on its speed.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Year** & **Speed (FPS)** & **GPU** & **Author** \\  & & & & **Real-Time** \\ \hline SwinDePose [83] & 2023 & 0.476 & V100 & \\ [PERSON] et al. [58] & 2019 & 0.5 & & \\ DeepVCP [59] & 2019 & 0.5 & & \\
3 DNL [73] & 2023 & 1.0 & A100 & \\ Yolo 7 [84] & 2023 & 1.0 & GTX3090 & \\ Improved CDPN [77] & 2023 & 1.33 & 3070 & \\ EPOS [62] & 2020 & 1.33 & P100 & \\ GEN6D [38] & 2022 & 1.56 & 2080 Ti & \\ CosyPose [14] & 2020 & 3.0 & & YES \\ [PERSON] et al. [81] & 2022 & 5.05 & GTX 1080 Ti & \\ [PERSON] et al. [48] & 2023 & 7.0 & & YES \\ ZebraPose [37] & 2022 & 9.0 & & YES \\ SSD6D [6] & 2017 & 10.0 & GTX 1080 & YES \\ Pix2 Pose [10] & 2019 & 10.0 & GTX 1080 & YES \\ BundleTrack [19] & 2021 & 10.0 & GTX 1080 Ti & YES \\ DeepIM [61] & 2018 & 12.0 & 1080 Ti & \\ PoseRBPF [11] & 2019 & 12.2 & TITAN X (Pascal) & YES \\ FFB6D [26] & 2021 & 13.3 & & YES \\ SO-Pose [23] & 2021 & 20.0 & TITAN X (Pascal) & YES \\ OVE6D [31] & 2022 & 20.0 & GTX 3090 & YES \\ PoET [43] & 2022 & 20.0 & GTX 3090 & YES \\ PLuM [47] & 2023 & 20.0 & GTX 3090 & YES \\ FS-Net [21] & 2021 & 20.0 & GTX 3090, RTX 2060 & YES \\ FS6D-DPM [28] & 2022 & 20.0 & & YES \\ DFTr network [42] & 2023 & 20.0 & & YES \\ CoS-PVNet [51] & 2024 & 21.0 & & YES \\ G2L-Net [15] & 2020 & 23.0 & GTX 1080 Ti & YES \\ SE-UF-PVNet [49] & 2023 & 23.6 & GTX 3090 & YES \\ ASPP-DF-PVNet [18] & 2021 & 25.0 & Tesla V100 & YES \\ RNNPose [86] & 2024 & 26.62 & & \\ Compressed YOLO-6D [40] & 2019 & 27.0 & & YES \\ Focal Segmentation [53] & 2024 & 29.0 & GTX 3090, RTX 2060 & YES \\ HybridPose [16] & 2020 & 30.0 & GTX 1080 & YES \\ MV6D [30] & 2022 & 30.0 & Tesla V100, RTX 2080 Ti & YES \\ BB8 [5] & 2017 & 31.0 & TITAN X (Pascal) & YES \\ EPro-PnP [52] & 2022 & 31.0 & & YES \\ [PERSON] et al. [39] & 2023 & 31.0 & & YES \\ DPOPV2 [27] & 2022 & 32.0 & GTX 1080 Ti & YES \\ CloudAAE [20] & 2021 & 33.0 & TITAN X (Pascal) & YES \\ \hline \hline \end{tabular}
\end{table}
Table 12: Model Speeds and GPU Usage.  
15 models, suggesting that a significant number of models operate at lower speeds not able to perform real-time tasks. The 20 FPS have 10 models and 30 FPS have 9 models, indicating moderate performance clusters. As the speed increases, the number of models decreases, with 6 models achieving 50 FPS and only 4 models exceeding 50 FPS, signifying that higher-speed models are less common and potentially more optimized for real-time performance.