## 5 Deep-Learning-Based Methods for Point Cloud Understanding
### Point-Based Methods
This is achieved by dynamically assigning attention weights to different neighboring points and feature channels based on their spatial positions and feature differences. [PERSON] et al. [153] tested a plug-and-play point global context reasoning (PointGCR) module, which can help to capture context information along the channel dimension by using an undirected graph representation and self-attention mechanism. The authors show a significant boost in performance with the PointGCR module appended to several famous encoder-decoder networks for point cloud segmentation on outdoor and indoor scenes. [PERSON] et al. [154] published the multi-resolution graph neural network (MuGNet) architecture, which translates large-scale point clouds into directed connectivity graphs and efficiently segments the point clouds with a bidirectional graph convolution network. Using a multi-resolution feature fusion network reduces memory consumption but conserves rich contextual relationships. The success of this algorithm was its ability to train deep networks reliably. However, training very deep GCNs comes with certain shortcomings. First, stacking multiple GCN layers leads to the vanishing gradient problem, the same as in CNNs. Second, the over-smoothing problem can occur when repeatedly applying many GCN layers [155]. Thus, most state-of-the-art GCNs are limited to shallow network architectures, usually no deeper than four layers [156; 157]. **Point-based recurrent neural network** (RNN) methods aim to improve scene understanding by treating the point input as a sequence of features. RNNs have proven to be highly effective for processing sequence data with variable lengths, like sensor data streams, human speech, and written text [158]. Few attempts exist to leverage the power of RNNs and the ability to memorize the prior input for point cloud semantic segmentation. [PERSON] et al. [159] improved on the first PointNet [17], which is bound to subdivide larger point clouds into a grid of blocks and process each block individually. In the proposed network, one recurrent consolidation unit (RCU) inputs a sequence of block features originating from four spatially nearby blocks and returns a sequence of corresponding updated block features. The RCU is configured as a gated recurrent unit (GRU) where updated block features are returned only after the GRU has seen the whole input sequence. The GRU retains relevant information about the scene in their internal memory and updates it according to new observations. [PERSON] et al. [160] (RSNet) suggested a lightweight local dependency module that uses three slice pooling layers along the X, Y, and Z axis to convert unordered point feature sets into an ordered sequence of feature vectors. By modeling  
Figure 17: Visualization of individual steps in the superpoint graph pipeline. **Left**: geometric partitions from superpoints. **Right**: visualization of the interconnecting superpoint graph (SPG); originally shown in Landrieu and Simonovsky [149]. one slice as one timestamp, the information interacts with other slices as the information flows through the RNN internal state (memory). Comparably, [PERSON] et al. [161] (3P-RNN) utilized a combination of pointwise pyramid pooling to capture local geometries and a two-directional sliding window hierarchical RNN to explore long-range spatial dependencies in the X and Y direction. To this extent, the above RNN-based methods rely on static pooling to aggregate features from the local neighborhood, without adapting to density changes [162]. Similar to the aforementioned dense voxel discretization methods, this leads to computational inefficiencies and poor segmentation results. At this point, the latest state-of-the-art benchmark results, like S3 DIS's large-scale indoor scene benchmark [59] (Figure 13), show that RNN-based and GCN-based methods lately cannot keep up with modern point-based convolution, MLP-pooling, and transformer-based methods.