# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results  
### Ablation Study  
We conducted an ablation study on the LineMOD dataset to evaluate the effectiveness of each proposed module in terms of accuracy, parameter count, and real-time inference speed. The results are summarized in Table 2, where the accuracy metric is based on ADD-(S) (ADD is used for asymmetric objects, while ADD-S is applied to symmetric ones). The number of parameters reflects the model's complexity, and the runtime (FPS) indicates its inference efficiency. DenseFusion is used as the baseline model. The MGC column denotes the use of multi-branch graph convolution modules, while SA represents the self-attention mechanism. These two components together form the multiscale point cloud feature extraction module. LIFE refers to the lightweight image feature extraction module, which replaces the original image downsampling layers in DenseFusion with MobileNetV2. As LIFE is an independent replacement of the image encoder, we did not further break it down into sub-components in the ablation experiment. From the results in Table 2, we can observe that each proposed module contributes positively to model performance in terms of accuracy and efficiency. Below, we provide a detailed analysis of the effects of the multiscale point cloud feature extraction module (MGC + SA) and the lightweight image feature extraction module (LIFE), as well as the trade-offs between accuracy, parameter count, and runtime speed. Comparing the baseline (Row 1) with the variant incorporating only MGC (Row 2), we observe a significant improvement in accuracy from 94.3% to 96.4%, indicating the benefit of introducing multi-branch graph convolution. This module enhances the local geometric representation by aggregating features from multiple receptive fields. However, it introduces a moderate increase in parameters and a slight drop in FPS due to the added computational paths. When the self-attention (SA) module is further added (Row 3), the accuracy rises to 97.6%, showing that the adaptive weighting of multi-scale features can better emphasize task-relevant patterns. Notably, despite the added attention mechanism, the overall parameter count decreases slightly compared to Row 2 due to structural balancing. This confirms that the SA module improves the feature selection efficiency with only a minor increase in overhead, demonstrating a favorable trade-off between accuracy and complexity. Replacing the original CNN backbone with MobileNetV2 (Row 4) significantly reduces the parameter count from 23.39M to 18.86M and boosts the inference speed from 25.5 FPS to 35.3 FPS. However, this comes with a slight drop in accuracy (94.1%), indicating that aggressive compression may compromise the representation power if not complemented by other enhancements. When LIFE is combined with MGC (Row 5), the accuracy climbs to 97.5% while maintaining a relatively low parameter count (22.48M) and high speed (32.6 FPS). This suggests that point cloud enhancements can \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**MGC** & **SA** & **LIFE** & \begin{tabular}{c} **Accuracy** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **Parameter** \\ **(M)** \\ \end{tabular} &
\begin{tabular}{c} **Runtime** \\ **(FPS)** \\ \end{tabular} \\ \hline  & & & 94.3 & 23.39 & 25.5 \\ ✓ & & & 96.4 & 27.31 & 22.8 \\ ✓ & ✓ & & 97.6 & 25.83 & 22.4 \\  & & ✓ & 94.1 & 18.86 & 35.3 \\ ✓ & & ✓ & 97.5 & 22.48 & 32.6 \\ ✓ & ✓ & ✓ & 98.5 & 19.49 & 31.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of ablation experiment results. compensate for lightweight image features, resulting in a balanced and effective design. Finally, integrating all three components (Row 6) achieves the best accuracy (98.5%) with the fewest parameters (19.49 M) among the high-performing variants, confirming the effectiveness of the full architecture.