## 2 Related Work
### Fully Supervised Methods on 3D Point Clouds  
Three-dimensional point clouds of buildings generally represent complex geometric structures, where the semantic content is not directly included. Therefore, semantic segmentation of it is still a challenging task. In traditional _data-driven_ approaches [25; 26], points with some notions of similarity are clustered together to map point clouds into classes by constructing feature descriptors (e.g., verticality, planarity and elevation). In conventional _model-fitting_ approaches [27; 28; 29], some geometric models are sought to detect specific objects, such as houses, roofs, trees, etc. Despite the impressive performances from these traditional approaches, models or geometric descriptors cannot interpret the complexity of real data. Moreover, conventional semantic segmentation approaches heavily rely on hand-crafted features, making the generalization difficult. Thus, their efficient application to obtain high-LoD building models still remains quite challenging. Due to these reasons, the chance of using DNN-based _fully supervised learning_ to effectively and automatically extract features in an end-to-end fashion gives rise to the application of these promising methods for semantic segmentation of buildings' point clouds. Based on the DNN input data format, existing point-cloud semantic segmentation methods can be grouped into _direct_ and _indirect_ methods. The latter usually first partition the 3D space into regular representations such as _image_[30; 31; 32] or _voxel_[33; 34] data structures to take advantage of well-established 2D/3D DL networks for feature learning and semantic segmentation. However, due to point clouds' inherent nature, the process of transferring point clouds to another intermediate representation degrades the resolution of the measured objects, which would result in quantization error and inefficiency [35]. In contrast, direct methods do not introduce explicit information loss [36]. More recently, the pioneering direct method PointNet [35] was proposed. It directly operates on point clouds, using a Multilayer Perceptron (MLP) to learn high-dimensional features for each point independently. Subsequently, pointwise features are stacked to a global feature through a max pooling layer. Since pointwise features are learned individually from each point in PointNet, the local context information between points is ignored. This work was subsequently extended in a variety of ways to extract local information within a point cloud. For example, Dynamic Graph Convolutional Neural Network (DGCNN) [21] improves the performance of segmentation by considering the relations between points in the local neighborhoods and by aggregating them into a global feature in the EdgeConv layers, which can be plugged into existing architectures. Since current state-of-the-art methods have shown that aggregating local and global information may increase the network's capabilities of capturing context information, our network will exploit the feature extraction power of EdgeConv layers, which directly consumes points and incorporates the local neighbor information obtained from point clouds. DL techniques for 3D point-cloud segmentation have been successfully applied in recent years, while the development in the built-environment domain has just started to be explored. A limited number of studies use DL methods to segment point clouds of buildings. Compared to the improvement of DL methods in indoor scenes, the segmentation methods of high-LoD buildings' point clouds are still at the initial stage of development. Most existing studies focus on LoD1 [15; 24; 29; 37; 38], LoD2 [39; 40] or one category of building elements [41]. Moreover, as was pointed out in the introduction of this paper, only the ArCH dataset [17] with pointwise annotations is publicly available. The process to acquire pointwise point clouds is also very time-consuming. For instance, the period required for manually labeling the ArCH dataset is one month [3].