# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 2 Related Works
### Attention Mechanism  
The attention mechanism was initially introduced in machine translation and has since become a fundamental component in both image processing and natural language processing tasks. In models with large parameters or information overload, attention mechanisms play a crucial role, allowing the network to focus on the most relevant information while filtering out irrelevant or redundant inputs. This targeted focus enhances both the computational efficiency and task accuracy. A representative example of this is SENet [37], which models inter-channel dependencies to automatically learn the importance of each channel, thereby enhancing useful features and suppressing less informative ones. CondConv [38] extends this concept by introducing dynamic convolutional kernels that adapt to each input sample, improving the model capacity without sacrificing inference speed. Further, SKNet [39] incorporates convolutional kernels with varying receptive fields, enabling the network to adaptively process input features across multiple scales.  
Building upon the foundation of attention, self-attention expands its capability by enabling each input feature to interact with all others. This not only captures global contextual relationships but also allows the model to dynamically adjust feature importance, making it particularly suitable for complex tasks. In the domain of 6D pose estimation, self-attention has shown significant advantages, especially in multiscale feature fusion. For instance, Trans6D [40] introduces a self-attention mechanism to effectively aggregate multiscale features from RGB-D data, improving the capture of global semantic information while reducing the influence of irrelevant inputs. This design enhances robustness in scenes with complex backgrounds and partial occlusions. Similarly, SaMfENet [41] leverages self-attention to strengthen the representation of multiscale geometric features in point clouds, leading to an improved pose estimation performance in the presence of occlusions and in low-texture conditions. These studies demonstrate the potential of self-attention mechanisms to achieve robust and accurate multimodal feature fusion in challenging environments.  
Motivated by the effectiveness of transformer architectures in large-scale vision models, recent studies on 6D pose estimation have also explored transformer-based strategies to further enhance global feature interactions. One such example is the deep fusion transformer network (DFTr) [42], in which a transformer-based fusion approach is adopted to aggregate contextual information across spatial locations and pose predictions are refined using a weighted keypoint voting mechanism. By modeling both local and global dependencies, it achieves improved generalization across object categories and varying scenes. However, the high computational cost of transformer modules and the complexity of keypoint voting significantly hinder its real-time performance and practical deployment on resource-limited devices.  
In contrast, the self-attention-driven multiscale feature fusion mechanism adopted in this study avoids explicit keypoint voting and complex transformer structures. Instead, a lightweight yet effective strategy is employed to dynamically aggregate spatial features while preserving both local geometric detail and the global semantic context. This design offers a favorable trade-off between computational efficiency and pose estimation accuracy. As a result, the proposed method exhibits strong robustness in the face of occlusions, textureless surfaces, and complex environments, making it highly suitable for real-time 6D pose estimation applications.