## 6 Discussion
The statistics about class distribution, as proposed in Section 2, in terms of points per class as well as instances per class, help to keep an overview and to counteract iteratively in the case of disequilibrium. From the findings in the first half of this paper, we advocate for the community to put more effort into the creation of industry-standard datasets and for the recipients to honor the hard work of developing datasets. Synthetic data are considered a complementary solution against dataset scarcity because synthetic data annotation is essentially free [180]. Table 1 features synthetic datasets and singular attempts that utilize synthetic training data to improve real-world performance. An outstanding example is the VASAD dataset [22] for building reconstruction. Researchers argue that the transfer of knowledge can improve the ability to perform complex tasks when initially performed in simulation [181, 182]. Furthermore, the class imbalance problem becomes obsolete if class-object appearance in scenes is user-defined. However, experience with synthetic data to improve point cloud semantic segmentation is still limited, and studying their effects is currently under investigation. For example, the effect known as the \"Sim2 Real (sim-to-real) gap\" [183, 182] describes the discrepancy between simulated and real environment data, a phenomenon that can result in bad performance if training the network with synthetic data but applying the model to the real world [184]. Unlike training data, which are content and context-locked, general machine learning models can be transferred independently on their initial application as this survey shows. Algorithms initially developed to segment, e.g., cars and pedestrians, can be successfully deployed to segment buildings, if trained with the appropriate data. One objective of this survey has been to identify the future trends and most promising methods for PCSS to channel our future research and share this with everyone with the same intentions. Several authors described their methods as the most suitable for PCSS and scene understanding, but this review leads to the assumption that, still, no method proves to be dominant. Convolutional neural networks (CNNs) have traditionally been preferred for processing image data, while recurrent neural networks (RNNs) have been for sequential data. However, we find no consensus on the most appropriate method to analyze the 3D domain. Projection-based methods have legitimation in autonomous driving and mapping applications because of fast processing and their synergy with directed stereo cameras and spherical LiDAR if the demand for resolution is low. Discretization-based learning methods for PCSS appear to be not competitive due to memory overhead with fully connected CNNs and the lack of canonical order for permutation invariance. However, voxel discretization remains important for all kinds of point cloud processing [185] and map building in SLAM. The reviewed RNN methods for point cloud understanding struggle to adapt to changing point cloud density and rely on extensive partitioning procedures. Only a few approaches of this kind could be found to deal with large-scale PCSS. Like in natural language processing, transformers recently look more promising for the sequence datatypes [167]. Graph neural networks (GNNs) and graph convolution offer a natural representation of point clouds and claim to solve the issue of capturing long-range spatial context information. However, stacking multiple GCN layers leads to a vanishing gradient and over-smoothing. Autonomous vehicles and a network of sensors on our roads potentially soon produce a huge 3D roadmap dataset [186]. There is no expertise in this field for now, but GNNs might be a valuable approach for scene understanding in infrastructure civil engineering with low requirements for detail but the need to process huge spatial datasets.