## 5 Deep-Learning-Based Methods for Point Cloud Understanding  
Deep learning for computer vision in the 3D domain has garnered significant attention and interest in recent years, particularly since 2015, when various approaches emerged to extend the application of 2D convolutions for image object recognition to the 3D model space [118; 119; 120]. However, the transition from 2D raster images to 3D point clouds presents unique challenges due to the unordered and unstructured nature of point cloud data [121]. Different preprocessing steps are required to transform the raw point cloud data into a structured format that can be processed by a neural network. According to [PERSON] et al. [25], common learning-based PCSS approaches can be divided into three baseline categories: projection-based, discretization-based, and point-based methods. We adopted the taxonomy for 3D point cloud segmentation in Figure 12 and expanded them with the latest achievements in point-based methods. The choice to apply a particular type or a hybrid method depends on the problem to solve. In the following, we provide a concise overview to comprehend the basic characteristics of each method, followed by a discussion of the advantages and disadvantages of applying certain models in the construction industry, considering customary point density and cloud size for point clouds of building objects.  
Projection-based and discretization-based methods share a common initial step. Both transform the unstructured point cloud into a regular intermediate representation to enable data handling. This regular representation is usually a \(d\)-dimensional grid \(d\in\mathbb{N}^{+}\) (e.g., image with \(d=2\) and voxel with \(d=3\)), in which points can be referenced by indices, and the neighborhood relationship is defined by contiguousness. Semantic segmentation is performed on the structured data in the intermediate space, and the result is subsequently projected back to the original, unstructured point cloud. In contrast to projection and discretization-based methods, point-based methods operate directly on irregular point clouds without using an intermediate representation [25]. We evaluate the publicly available semantic segmentation results of 54 methods on the S3 DIS benchmark in Figure 13 on a timeline. For a performance comparison of the state of the art, we selected the mIoU as the most meaningful metric in the presence of class imbalance and the most adopted metric in public databases for PCSS tasks. The data for this comparison, secondary performance metrics (mIoU, mAcc, and oAcc), and an allocation of the deployed network architecture can be found in Table 11.  
Figure 12: Taxonomy of deep learning methods for 3D point cloud segmentation. Adopted from [25], and extended with the latest transformer-based methods.  
Figure 13: Reported results for semantic segmentation task on the large-scale indoor S3 DIS benchmark (including all 6 areas, 6-fold cross-validation) [59]. Performance is expressed in terms of mean Intersection over Union (mIoU). Item numbers represent the ranking within this database according to mIoU performance. The data corresponds to the evaluation in Table A1.