# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 4 Results
### Comparison of Algorithms Under the LineMOD Dataset  
For the LineMOD dataset, following previous work, we considered a prediction as correct if the ADD-(S) was less than 10% of the object's diameter. The percentage of correctly predicted keyframes out of the total number of keyframes was used as the evaluation metric (i.e., accuracy). The evaluation results for various algorithms are shown below in Table 3. Bold numbers represent the highest scores among the methods, and objects marked with an asterisk (*) are symmetric. Table 3 presents a comparative evaluation of our proposed method against several representative RGB and RGB-D based algorithms on the LineMOD dataset. Among the RGB-based approaches, PoseCNN+ICP and HRPose [49] achieved average accuracies of 88.6% and 87.6%, respectively, with both failing to surpass the 90% threshold. However, RNNPose exhibited a strong performance among RGB-only methods, with an average ADD(S) accuracy of 97.1%, indicating that even in the absence of depth data, it can produce competitive results through effective temporal modeling. For RGB-D-based methods, FS6D achieved an accuracy of 91.5% using few-shot learning techniques and RGB-D inputs. DenseFusion and MaskedFusion, which adopt dense pixel-level fusion strategies between RGB and depth features, achieved higher accuracies of 94.3% and 97.3%, respectively. These results highlight the benefit of effective geometric and visual feature integration for pose estimation robustness. Notably, DFTr further improves upon previous results, with an average accuracy of 99.2%, benefiting from transformer-based global feature aggregation and a cross-modal fusion design. In contrast, our proposed method reaches an average ADD(S) accuracy of 98.5%, outperforming PoseCNN+ICP by 9.9%, DenseFusion by 4.2%, and MaskedFusion by 1.2%. Although DFTr achieved a slightly higher mean accuracy, our method maintains a highly competitive accuracy while offering a better computational efficiency and a sig nificantly lower model complexity, making it more suitable for real-time or resource-constrained applications. From a category-wise perspective, the proposed method achieves the best performance in several challenging object categories such as ape, can, driller, duck, and hole, all of which contain occlusions or have weak textures. This superior performance is attributed to the proposed multiscale point cloud feature fusion module, which enhances local geometric detail extraction and captures spatial context more effectively. Furthermore, for symmetric objects such as eggbox and glue, the proposed method achieves or matches the highest possible accuracy (100%), demonstrating robustness in handling symmetry ambiguity. When comparing the experimental results, we observed that nearly all algorithms struggle to accurately estimate the poses of objects with indistinct surface textures, such as apes, drillers, and ducks. Due to their lack of effective texture features, RGB-based methods find it difficult to handle such objects. Using depth as supplemental information for iterative optimization can improve the accuracy to some extent; however, spatial geometric information is not fully integrated using this approach, resulting in a lower overall accuracy. Both DenseFusion and the proposed method use fused information from both images and depth data as the basis for pose estimation, taking advantage of the complementary nature of both data sources. Therefore, they both demonstrate good performances in pose estimation for objects with indistinct surface textures. Figure 8 shows a comparison of DenseFusion and the proposed method's results on several weak-textured objects. From the comparison between DenseFusion and the proposed method, it is evident that the proposed method exhibits significantly improved accuracy across nearly all object categories. This is because DenseFusion only extracts single point cloud features, which, while improving accuracy to some extent, still has its limitations.