## 5 Deep-Learning-Based Methods for Point Cloud Understanding
### Projection-Based Methods  
Projection-based methods render raster image intermediate data from spatial 3D point clouds to enable the use of powerful 2D convolutional networks for semantic segmentation [3]. Several approaches have been proposed to project the 3D space onto 2D surfaces to cover large landscapes.  
**Multi-view representation** approaches are among the earliest approaches for 3D data semantic segmentation. The limitations of 3D unstructured points can be avoided by taking snapshots of point cloud scenes with predefined image sizes, as shown in Figure 14a. These uniform, synthetic images can serve as input to any 2D CNN intended for semantic segmentation. The synthetic raster images may encompass features like trichromatic color values, XYZ-surface normals, and scalar field depth information [3]. One significant advantage of projection-based PCSS is the high availability of annotated training data since developers can revert to the knowledge base of standard image semantic segmentation resources and adopt pre-trained models through transfer learning [122; 123]. Nevertheless, only a few studies have used multi-view-based deep learning approaches for PCSS in construction because of fundamental limitations. First, the performance of multi-view PCSS methods is sensitive to viewpoint selection and occlusions. For large scenes, which are common in the construction environment, it is challenging to select adequate viewpoints and camera angles to cover the whole scene; hence, the projection step inevitably introduces information loss [25]. Besides their limitations and the intense competition among the different approaches (Figure 13), the latest DeepViewAgg model [124] produces competitive state-of-the-art semantic segmentation results by leveraging multi-view aggregation to merge features from images taken at arbitrary positions. This method has the advantage of not being dependent on colorized point clouds, facilitating a more general sensor choice and higher recording speed.  
**Spherical representation** methods project the whole point cloud, captured by a single scan, onto a sphere with the origin in place of the capturing device's position [127]. Several LiDAR sensors already represent the raw input data in a range-image-like fashion [128]. Because of its synergy with 360-LiDAR sensors, spherical projection representation, as shown in Figure 14b, has proven to be extremely valuable in autonomous driving applications [127; 128; 129], where the real-time and robust perception of the environment is indispensable, while point cloud density and a low level of detail are secondary. Because of its runtime advantage, [PERSON] et al. [130] called projection methods the \"de facto\" method for large-scale point cloud segmentation. Compared to single-view projection, spherical projection retains more information. However, this intermediate representation inevitably introduces several problems, such as discretization errors and blurred CNN outputs, due to subsampling to the resolution of the synthetic image [128]. If the center of the projection is not equal to the initial sensor's position, the method suffers from the same problems as multi-view representation, i.e., occlusion and translucency. Due to the dependence on the sample locations, projection methods are only suitable to a limited extent for practical  
Figure 14: Illustration of two projection-based methods. (**a**) Multi-view representation, snapshot planes; originally shown in [125]. (**b**) Spherical representation; originally shown in [126].  
application in the construction industry. No published benchmark results of projection methods are known for the labyrinthine S3 DIS indoor dataset to compare in Figure 13.