## 5 Deep-Learning-Based Methods for Point Cloud Understanding
### Point-Based Methods
**Point-based multi-layer perceptron networks** (MLPs) usually process each point individually through shared MLP to perform local feature aggregation. MLPs are highly efficient but often face difficulties in grasping the global context and spatial features of point clouds [17; 163]. Among the point-based MLP networks, state-of-the-art methods can be subdivided into pooling-based and attention-based transformer approaches, to capture a wider context for each point and learn richer local structures. _Pooling-based methods_ learn to consolidate features in the local neighborhood of a point group. The hierarchical neural network PointNet++ [18] can recognize fine-grained patterns and generalizes complex scenes by recursively applying PointNet on a nested partitioning of the input point set. The information from different neighborhood sizes is aggregated for each point of interest with neighboring feature pooling. Thus, MLP-based methods can improve general feature learning for non-uniform point density in different areas [18]. In contrast to PointNet++, which defines the neighborhood from the metric world space, [PERSON] et al. [164] employed two grouping mechanisms to define neighborhoods in the world space by clustering points using K-means, but also in the feature space, by computing the \(k\) nearest neighbors (KNNs). Assuming that points from the same semantic class are likely to be nearby in the feature space, they defined a pairwise similarity loss function and found with [165] that semantic similarity can be measured as a distance in the feature space. [PERSON] et al. [166] (BAAF-Net) resolved three major drawbacks of existing works that affect segmentation quality: ambiguity in close points, redundant features, and inadequate global representations. While most architectures are designed to solve a broad range of scene understanding tasks (including object detection and shape classification), the BAAF-Net architecture entirely focuses on large-scale point cloud semantic segmentation. This is conducted by the concept of augmenting the local context bilaterally and fusing multi-resolution features for each point adaptively. [PERSON] et al. [85] proposed a unified framework called PointMeta. Its building blocks can be summarized in terms of four meta functions: a neighbor update function, a neighbor aggregation function, a point update function, and a position embedding function (implicit or explicit). By modifying the existing approaches, [PERSON] et al. [85] derived a basic building block (PointMetaBase). PointMetaBase-XXL surpasses previous methods in several PCSS benchmarks (Figure 13) in the configuration with multiple stacked PointMetaBase blocks, max-pooling as neighborhood aggregation function, and Explicit Position Embedding (EPE adopted from Point Transformer [108]). _Transformer-based methods_, first introduced to the field of natural language processing (NLP) [167], are designed to process sequential input data. The Transformer is a decoder-encoder structure with three main modules for input (word) embedding, positional (order) encoding, and self-attention. The self-attention module is the core component that relates different positions of a single sequence to compute a representation of the sequence. Following the success of Transformer network architectures in the language and image domains, where competitive performance was achieved compared to their CNN counterparts [168; 169; 170], Transformers have lately been applied to process unordered 3D point clouds. Since point clouds essentially are sets of points with positional attributes, the self-attention mechanism seems particularly suitable for these data type [167; 108]. The simplest approach to modify the Transformer [167] for point cloud processing implies treating the entire cloud as a sentence and each point as a word. [PERSON] et al.