# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 3 Materials and Methods
### Overall Structure of the Network
#### 3.2.3 Multiscale Point Cloud Feature Extraction Module
Conversely, a smaller \(r\) preserves finer local geometric details but may result in the loss of the global structure, reducing the effectiveness for highly occluded or low-texture regions. To balance this trade-off, we adopt the multi-scale neighborhood definition strategy from PointNet++ grouping layers, selecting \(r\) with an interval of 0.2 to effectively capture features at different scales. The local feature \(F^{1}\) computation is as follows:  
\[F^{1}(i,j)=h(P(i)-Y(i,j))(i\in N,j\in k) \tag{2}\]  
where \(N\) represents the total number of sampled points in the point cloud, \(P(i)\) represents the \(i\)-th center point in the point cloud, \(k\) represents the number of points selected in the neighborhood, and \(Y(i,j)\) represents the \(j\)-th neighboring point of the \(i\)-th center point. \(h()\)denotes a nonlinear computation, i.e., a MLP. To obtain more effective local features, we introduce an attention mechanism that assigns an adaptive weight to each point's local feature representation. This weight consists of a self-weight \(\alpha\) and a local weight \(\beta\), where \(\alpha\) is derived by passing the global point cloud through a shared MLP with dimensions (3,128,1), representing the significance of each point independently. Meanwhile, \(\beta\) is computed by processing the extracted local features \(F\) through another shared MLP (128,1), assigning importance to the k-nearest neighbor points. The final attention coefficient \(\gamma\) is obtained by summing \(\alpha\) and \(\beta\) and further refining it through a shared MLP to generate the final local coefficients. These computed attention coefficients are then used to weight the local feature vectors \(F^{1}\), dynamically emphasizing more relevant geometric structures. The weighted features are then summed along the neighborhood dimension, resulting in the final geometry-enhanced output of the  
Figure 4: Neighborhood feature extraction structure. graph convolution layer, \(F^{2}\), which captures both local and global dependencies in a more structured manner. The computation process is as follows:  
\[\left\{ \begin{aligned} \gamma(i,j)&=h^{\prime}(F^{1}(i,j))+h^{ \prime\prime}(P(i))\\ F^{2}_{i}&=\sum_{j=1}^{k}F^{1}(i,j)\times\gamma(i,j) \end{aligned} \right. \tag{3}\]  
where \(F^{2}\) represents the local features obtained by each graph convolution layer; \(P\), \(F^{1}\), \(i\), and \(j\) have the same definitions as in Equation (2); and \(h^{\prime}()\) and \(h^{\prime\prime}()\) represent the mapping functions used to calculate the self-weight and local weight. To ensure the network extracts the optimal local features, a self-attention module is introduced after the parallel graph convolution layers. When passing through this module, the feature vector \(F^{2}\) is assigned a specific weight, and, after passing through an average pooling layer, fully connected layers, and a softmax layer, the final local feature vector \(F^{3}\) is output. This process can be expressed as follows:  
\[F^{3}=\sum_{i=1}^{n}W_{i}\cdot F^{2}_{i},\sum_{i=1}^{n}W_{i}=1 \tag{4}\]  
Finally, the point-level features and local features \(F^{3}\) are concatenated to form the final geometric features of the point cloud.