# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 2 Related Work
### Semantic Boundary  
In 2D image vision tasks, boundaries were initially a concern, especially in the medical field [43; 44]. However, few studies noted the impact of semantic boundaries on holistic point cloud segmentation. Research has shown that boundary points are more likely to be misclassified than those in inner areas [12]. Therefore, it is very important and challenging to improve performance on the semantic boundary. GAC [40] learns the convolution weights from the feature differences between the center point and its neighboring points, thus guiding the convolution kernel to distinguish the boundary location. The boundary areas delineating skeletons provide basic structural information, while the extensive inner areas depicting surfaces supply the geometric manifold context. Therefore, GDANet [45] divides the holistic point cloud into high-frequency (contour) components and low-frequency (flat) components, paying attention to different types of components when extracting geometric features, so that the network can capture and refine their complementary geometries to supplement local neighboring information. BEACon [10] designs a boundary embedded attentional convolution network, where the boundary is expressed through geometric and color changes to influence the convolution weights. These studies considered the boundary implicitly in segmentation backbones.  
IAF-Net [6] categorizes areas that are hard to be segmented into three types: boundary areas, confusing interior areas, and isolated small areas. It can adaptively select points in these areas and specifically refine their learned features. It is well known that semantic boundaries cannot been adopted a priori at the testing stage. Therefore, to utilize boundary information explicitly, one common workaround is to add an extra boundary prediction module (BPM) to predict the semantic boundary, and then use these predictions as auxiliary information in the segmentation backbone. To prevent the local features of different categories from being polluted by one another, an independent BPM module was proposed in [8] to predict point cloud boundaries. The predicted boundary information is utilized as an auxiliary mask to assign different weights to different points during feature aggregation, thus preventing the propagation of features across boundaries. JSENet [7] jointly learns the semantic segmentation and semantic edge detection tasks. However, these methods are not suitable for unstructured environments, which usually feature unclear semantic edges. To this end, the authors of [11] designed cascaded edge attention blocks to extract high-resolution edge features, and then fused the extracted edge features with semantic features extracted by the main segmentation branch. These methods utilize boundary information explicitly to improve performance on the boundary, but the newly embedded boundary prediction modules greatly increase complexity. On the other hand, the numbers of boundary points and inner points vary hugely, which is a challenge for binary boundary/inner classification. To improve performance on the boundary with no need for complex modules, CBL [12] optimizes the representations learned by LAOs through contrastive learning on the boundary point, enhancing its similarity with neighboring points belonging to the same category in the representation space. However, it ignores the relationship between the boundary point and its neighboring points in inner areas.  
Unlike the abovementioned studies, this paper explores the relationship between the boundary point and its neighboring points in inner areas, proposing a lightweight framework for improving the segmentation accuracy of boundary points with no need for additional modules.