# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Real-Time Performance and Efficiency Evaluation  
To comprehensively evaluate the real-time performance and efficiency of the proposed method, we compared the inference speed, model size (in terms of parameters), and pose estimation accuracy (measured using ADD(S)) across several representative algorithms on the LineMOD dataset. The inference speed is expressed in FPS, indicating the number of keyframe images processed by each model per second in a full end-to-end pipeline. Table 6 summarizes the performance of seven commonly used methods, covering RGB-based, RGB-D-based, and large-scale vision models. The results show that HybridPose and RNNPose, as RGB-based methods, achieve inference speeds of 113.3 FPS and 87.1 FPS, respectively, with relatively small model sizes of 4.7M and 11.9M parameters. These models demonstrate excellent real-time capabilities but exhibit limited ADD(S) accuracies (91.3% and 97.1%, respectively) due to their lack of depth information. In contrast, RGB-D-based methods such as MaskedFusion, DFTr, and DenseFusion achieve significantly higher ADD(S) accuracies of 97.3%, 99.2%, and 94.3%, respectively, through the incorporation of geometric depth cues and multimodal fusion. However, this comes at the cost of increased model complexity. For example, DFTr introduces 162.1M parameters and has an inference speed \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Method** & **HybridPose [22]** & **RNNPose [27]** & **MaskedFusion [35]** & **DFTr [42]** & **SAM-6D [52]** & **DenseFusion [13]** & **Ours** \\ \hline Inference Speed (FPS) & **113.3** & 87.1 & 23.5 & 20.6 & 0.2 & 25.5 & 31.8 \\ Parameters (M) & **4.7** & 11.9 & 37.4 & 162.1 & \textgreater{}1000 & 23.4 & 19.5 \\ ADD(s) Accuracy (\%) & 91.3 & 97.1 & 97.3 & **99.2** & - & 94.3 & 98.5 \\ \hline \hline \end{tabular}  
* **Notes:** The bold numbers represent the fastest inference speed, the smallest number of parameters, and the highest ADD(s) Accuracy among the methods.  
\end{table} 
Table 6: Comparison of inference speed, model size, and pose accuracy across different methods. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **PVNet [21]** & **HybridPose [22]** & **GDR-Net [51]** & **CRT-6D [25]** & **SAM-6D [52]** & **Ours** \\ \hline ape & 15.8 & 20.9 & 46.8 & **53.4** & - & 40.2 \\ can & 63.3 & 75.3 & 90.8 & **92** & - & 76.2 \\ cat & 16.7 & 24.9 & 40.5 & 42 & - & **42.7** \\ driller & 65.7 & 70.2 & **82.6** & 81.4 & - & 80.6 \\ duck & 25.2 & 27.9 & 46.9 & 44.9 & - & **50.2** \\ eggbox * & 50.2 & 52.4 & 54.2 & 62.7 & - & **63.9** \\ glue * & 49.6 & 53.8 & 75.8 & **80.2** & - & 77.3 \\ holepuncher & 39.7 & 54.2 & 60.1 & 74.3 & - & **76.3** \\
**MEAN** & 40.8 & 47.5 & 62.2 & 66.3 & **66.5** & 63.4 \\ \hline \hline \end{tabular}  
* **Notes:** Bold numbers represent the highest scores among the methods, and objects marked with an asterisk (*) are symmetric.  
\end{table} 
Table 5: Evaluation results of various algorithms using the Occlusion LineMOD dataset. MaskedFusion and DenseFusion have moderate sizes (37.4M and 23.4M, respectively) but relatively lower speeds (23.5 FPS and 25.5 FPS, respectively). SAM-6D, which relies on a large-scale vision backbone, has the highest parameter count (>1000M) but suffers from an extremely low inference speed (0.2 FPS), rendering it impractical for real-time applications despite its strong performance in static scenarios. Our proposed method achieves a better balance between efficiency and accuracy. Optimized from DenseFusion, it attains a high ADD(S) accuracy of 98.5% while maintaining a lightweight architecture of only 19.5M parameters and achieving a real-time inference speed of 31.8 FPS. Compared with MaskedFusion and DFTr, our method offers a competitive or superior efficiency, with only minor sacrifices in accuracy, making it more suitable for resource-constrained real-time applications. In summary, RGB-based methods exhibit high-speed inference but fall short in accuracy due to the absence of depth cues.