# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 2 Related Works
### Pose Estimation
#### 2.1.2 Pose Estimation Methods Based on RGB-D Images  
The advent of RGB-D sensors allowed deep learning models to leverage richer information. Early methods treated RGB and depth information separately, with depth information only being used as supplementary data to refine the estimation results. For instance, methods such as PoseCNN and BB8 first estimate the object's pose using RGB images and then refine the results using point clouds generated from depth information, employing algorithms such as iterative closest point (ICP) [28] or generalized ICP (GICP) [29]. Another approach is to concatenate depth and RGB information and feed the combined features into the pose estimation network [30; 31]. However, such simple concatenation does not fully utilize the complementarity between RGB and depth data. Recent studies have focused on the inherent correlation between RGB and depth information. PointFusion [32] independently processes RGB image data and raw point cloud data using CNN and PointNet architectures, respectively, and fuses the outputs in a new fusion network. This network uses the input 3D points as spatial anchors to predict multiple 3D bounding boxes and their confidence scores. DenseFusion further improves upon this by fusing pixel-level features from both modalities and re-encoding the fused features for pose estimation. PVN3D uses a similar approach by combining RGB and point cloud information, feeding the fused pixel data into a full-pixel voting network to generate 3D keypoints. The 6D pose is then computed using the least squares method, addressing occlusion in pose estimation through keypoint detection.  
In recent research, RGB-D fusion has been extensively explored, emphasizing the correlation and complementarity between these two modalities to enhance the pose estimation accuracy and robustness. Various approaches have been developed to leverage both geometric and visual information. SurfEmb [33] improves pose estimation for complex-shaped objects by learning geometric and visual features through surface embedding, enhancing both accuracy and generalization. CMCL6D [34] adopts cross-modal collaborative learning, effectively fusing RGB and depth data to maintain high accuracies under varying lighting conditions and occlusions. Similarly, MaskedFusion [35] addresses occlusion challenges by employing an occlusion-aware mechanism and pixel-level fusion, significantly boosting the performance in cases of partial occlusion.  
Among these approaches, CMFF6D [36] features a cross-modality multiscale feature fusion framework that progressively integrates high-resolution RGB features with depth-based geometric representations via a multi-branch architecture. This design enhances informative features while suppressing noise, thereby improving robustness in the presence of occlusions and in textureless scenarios. However, explicit cross-modal fusion introduces a considerable computational overhead, which limits its real-time performance and poses challenges for deployment on resource-constrained devices. In contrast, our method adopts a self-attention-driven multiscale feature fusion strategy that simplifies the fusion process by dynamically aggregating spatial information across multiple scales. This unified design significantly reduces the computational complexity while maintaining high pose estimation accuracy, offering an efficient and robust solution for real-time 6D pose estimation in complex environments.