## 5 Deep-Learning-Based Methods for Point Cloud Understanding
### Point-Based Methods
[171] propose a naive point cloud Transformer (PCT) by implementing a coordinate-based point embedding, which ignores interactions between points and instantiates the attention layer with the self-attention proposed in the original Transformer [167]. Without further modification and purely based on coordinate features, the naive PCT was capable to outperform state-of-the-art methods. [PERSON] et al. [172] presented a point attention Transformer (PAT) with two core operations: group shuffle attention (GSA) for mining relations between elements in the feature set and gumbel subset sampling (GSS), to select a representative subset of input points. To improve frame rates in real-time applications, [PERSON] et al. [146] published RandLA-Net, a lightweight architecture that directly infers per-point semantics for large-scale point clouds. It uses random point sampling instead of more complex and computationally heavy point-selection approaches. RandLA-Net introduces a novel local feature aggregation module that increases the receptive field for each 3D point and leverages attentive pooling to preserve local features and overcome the loss of key features expected from random sampling. Besides the significant downsampling, the method can retain features necessary for accurate segmentation. [PERSON] et al. [173] designed a Point Transformer architecture that utilizes a SorthNet module and global feature generation to extract local and global features and relates both representations by introducing the local-global attention mechanism. The output of Point Transformer is a sorted and permutation-invariant feature list that can directly be incorporated into common computer vision applications. With the same name, [PERSON] et al. [108] proposed another Point Transformer architecture, entirely based on vector self-attention and pointwise operations, as a general backbone for several 3D scene understanding tasks. These encoder and decoder structures consist of modular point transformer layers, stacked in multiple stages with transition layers for down- and upsampling. For dense prediction tasks such as semantic segmentation, Point Transformer adopts a U-Net [174] design, as shown in Figure 18. The number of stages and the sampling rates can be varied depending on the application, e.g., to construct a lightweight backbone for fast processing. To overcome its early limitations, [PERSON] et al. [175] proposed group vector attention, a revised Point Transformer V2 architecture with improved position encoding and with an efficient partition-based pooling scheme. [PERSON] et al. [176] proposed a Stratified Transformer to compensate for the drawbacks of the Point Transformer (V1) [108] and improve on capturing long-range context in point clouds by using standard multi-head self-attention [167]. Partitioning a point cloud into non-overlapping cubic windows limits the effective receptive field (EFR) [177] to the local region, which causes false predictions. Enlarging the window size helps to increase the respected area, but comes with a higher memory cost. To extend the attention beyond the limited local region, [PERSON] et al. [176] used a stratified strategy to ensure that the subgroup (strata) is adequately represented. Points within a small window next to a query point are sampled densely. Points within a second large window are sampled sparsely as a trade-off between EFR and memory consumption. [PERSON] et al. [107] built upon Stratified Transformers and achieved state-of-the-art point cloud semantic segmentation results on the S3 DIS large-scale indoor scene benchmark, as shown in Table 11, with leading accuracy scores on structural component classification (ceiling, floor, wall, and column). Improvements are gained from the proposed window normalization with prior knowledge to account for variant local neighborhood point cloud density in the downsampling step.