# Boundary-Inner Disentanglement Enhanced Learning for Point Cloud Semantic Segmentation
## 2 Related Work  
### Semantic Segmentation  
Semantic segmentation of a point cloud is aimed at assigning each 3D point to an interpretable category. Recently, methods based on deep learning have gradually replaced traditional methods that rely on handcrafted features. They can automatically learn high-dimensional features, realizing end-to-end semantic classification. These methods can be classified into three types based on input data formats: voxel-based [14; 15; 16; 17; 18], multi-view-based [19; 20; 21; 22; 23; 24; 25; 26], and point-based [27; 28; 29; 30; 31; 32; 33; 34].  
To process 3D data, one typical approach is to store the point cloud in voxel grids and apply 3D convolution directly [14]. However, limited by acquisition techniques, the points in the point cloud are usually not distributed homogeneously, making most voxel grids unoccupied. Therefore, an unmodified dense 3D convolution on sparse grids is inefficient. To solve this problem, SS-CNs [15] was proposed as a sparse convolution operator to deal with sparse point clouds more efficiently. On the other hand, OctNet [16] partitions 3D space hierarchically using a set of unbalanced octrees, allowing more memory and computation resources to be allocated to relatively dense regions. This achieves a deeper network without prohibitive high resolution. However, transforming a point cloud into voxels is both memory-unfriendly and computation-inefficient, and this process can inevitably discard a lot of geometric information. Another approach is to project the point cloud into multiple views on which the de facto standard 2D convolution can be adopted directly [19; 20; 21; 22; 23; 24; 25; 26]. However, this kind of method is highly independent on projection position and angle, thus becomes a suboptimal choice for large-scale point cloud semantic segmentation.  
PointNet [33] pioneered the original research on point clouds without any data transformations. It independently learns point features with pointwise multilayer perceptions (MLPs). Despite being permutation-invariant, it fails to capture local context and performs poorly on complex scenes. PointNet++ [34] was a further optimization of PointNet. It adopts hierarchical multiscale feature aggregation structures to extract local features, which can significantly improve the overall accuracy. It also provides a de facto standard paradigm for subsequent segmentation networks, which mostly comprise subsampling, LAO, and up-sampling modules. RSNet [35] splits the point cloud into many ordered slices along the x-, y-, and z-axes, on the basis of which global features are pooled. Then, the learned orderly feature vectors are processed with a recurrent neural network. However, such MLP-based methods do not fully consider the relationship between points and their local neighbors, limiting their ability to capture local contexts [36]. It is well known that local contextual information is crucial for dense tasks, such as semantic segmentation. Recently, much effort has been made for effective LAOs, enabling researchers to explore and make the most of local relationships. Among them, pseudo-grid-based methods [37; 38; 39; 31] and adaptive-weight-based methods [28; 30; 40; 41] are widely used. Akin to 2D convolution for image pixels, pseudo-grid-based methods associate the weight matrix with predefined kernel points. However, the pseudo-kernel points must be defined artificially, which limits model generalizability and flexibility on different datasets. In contrast, adaptive-weight-based methods learn the convolution weight from features and the relative position relationship through MLPs.  
Although these delicately designed LAOs work well, experiments have shown that they already describe the local context sufficiently with saturated performance [42]. Therefore, this paper turns to another direction, focusing on semantic boundaries, which are usually overlooked in current segmentation networks.