## 6 Discussion  
With this survey, we have shown that point cloud semantic segmentation has made great strides through recent developments in machine learning. It has become a significant research area in computer vision, with broad applications in fields such as robotics, autonomous driving, and geodesy. The construction industry can greatly benefit from that. However, several challenges must be addressed before these methods can be fully integrated into industrial applications. In Section 3, we showed that there are currently no significant datasets available for construction-related point cloud learning. The state-of-the-art publications we surveyed used one of two workarounds to circumvent this shortage. Most publications focus on one of the few well-established datasets [59; 62; 131] for indoor scenes, which are, however, polluted by furniture and often sourced from outdated RGB-D sensors. Fewer publications established their private task-specific datasets [19; 24; 86] but avoid sharing this with the public. Not sharing the deployed data with the public makes the reproduction of the results impossible and complicates the further use of the results. This study highlights a significant challenge in the training of neural networks through supervised learning, wherein the availability of substantial human-labeled data remains crucial. Specifically, the creation of extensive, well-documented, and open-source datasets comprising point clouds combined with technical meta-data (such as component labels, object properties, and damage classification) emerges as a major hurdle. Despite conducting extensive research, viable alternatives to large annotated datasets for effectively training machine learning models have not yet been identified. Transfer learning [122; 123] is a powerful tool that can lower the amount of required training data; yet, context-specific data to fine tune the model does not become obsolete. The opposite is the case. Fine tuning a pre-trained model raises the demand for high-quality and versatile data [178; 179]. The need for data applies also to the required number of different datasets, designed for different sectors and applications in the construction industry. Sectors include building structures, civil engineering (over- and underground), infrastructure, and pre-cast fabrication. Applications include object detection, shape classification, semantic and instance segmentation, 3D reconstruction, and SLAM. The preparation of such datasets is a non-trivial task. First of all, it requires expensive hardware to capture the data. Secondly, human resources are needed to annotate data, an activity that is even more challenging in the 3D domain than in the 2D domain. Furthermore, domain experts are needed to create and review the dataset. Datasets must be extrinsically balanced (regarding architecture and style, location, and environmental changes) and intrinsically (in terms of their beholding classes). The huge efforts needed to create qualitative datasets have led companies to keep their elaborately compiled data under lock when data have become a valuable commodity in all industries. However, open-sourcing these data is necessary for interoperability (organizations or systems to work together), saving economic costs (avoiding collecting new data) and improving data quality (crowd-sourced debugging) and verification (reproducibility). Comprehensive and  
Figure 18: Point Transformer network architecture for semantic segmentation (**top**) and classification (**bottom**); originally shown in [108]. The encode–decoder structure is a U-Net [174] shape. insightful databases, like the one we proposed in Table 11, help make such data more visible and accessible and, thus, boost development. Balancing datasets retroactively is only possible to a limited extent, as we explain in Section 3. One lesson we learned from reviewing multiple datasets within this survey was to keep track of the object distribution already in the process of creating a new dataset.