## 1 Introduction  
The diffusion of buildings' point clouds at a high Level of Detail (LoD) [1] such as LoD3 provides very detailed geometrical representation and semantic information [2], which enables and promotes new applications in a variety of fields such as cultural heritage documentation and preservation [3, 4, 5], construction engineering [6, 7], emergency decision making [8] and smart cities [9]. However, point clouds of buildings generally provide the representation of the entire premises including only a few types of architectural elements with no semantic information, limiting the efficient exploitation in the abovementioned application domains [10]. Hence, it is essential to investigate the methods of extracting semantic information from buildings' point clouds to acquire high-LoD models [11]. Thanks to the growing resolution and accuracy of 3D laser scanning sensors, point-cloud analysis has attracted a lot of interest in research. Inspired by the success of _Deep Neural Networks_ (DNNs) used in Computer Vision (CV) to accomplish subset tasks (e.g., classification, detection and semantic segmentation), Deep Learning (DL) approaches have appeared in the last few years for understanding 3D point clouds [12]. Semantic segmentation is a fundamental task in 3D point-cloud analysis. As a result of the success in recent DL-based point-cloud analysis studies, DL-based approaches have been demonstrated to be a promising alternative to traditional segmentation methods. They aim to segment buildings by automatically learning features from labeled point clouds, rather than hand-craftedfeatures. This principle has been successfully applied in numerous applications, such as indoor [13], urban [14] and buildings' scenes [15]. Even though important results were achieved, the existing DL approaches for building point clouds are strongly supervised, and these methods have substantial demands for finely labeled data [16]. However, it is not feasible to create such an amount of labeled training data in many real-world problems. A building point cloud composed of thousands of 3D points would need to be individually annotated in the case of segmentation. This cumbersome process results in a lack of annotated 3D architectural datasets. For example, to the best of our knowledge, only the Architectural Cultural Heritage (ArCH) dataset [17] is publicly available to provide pointwise annotations and support for generating high-resolution LoD3 building models. Furthermore, in order to train a satisfactory segmentation network, billions of pointwise accurate labels are demanded [16], which is extremely time-consuming to obtain. For instance, some studies [3; 5] on the application of DL in architectural semantic segmentation have been done only after the setup of the fine-labeled ArCH dataset. In the CV domain, the problem of hunger for fine-labeled pointwise training data is often tackled by using _unsupervised_ methods. Unsupervised learning plays an essential role in the CV field as it provides a way to pre-train feature extractors on large unlabeled datasets. The pre-trained extractor may initialize the parameters of downstream networks for more efficient and effective training on downstream tasks [18]. However, these approaches are mostly designed for 2D images, which are fundamentally different from unordered point clouds. Unlike 2D images that are projective observations from the built environment, 3D point clouds provide a metric reconstruction of the building scenes without scale ambiguity [19]. Furthermore, the application of label-efficient unsupervised learning to downstream tasks in the 3D field is still limited to either single-object or low-level tasks such as registration and classification [20]. Therefore, from a scientific viewpoint, it is still an open issue when using unsupervised DL-based methods in the semantic segmentation task of buildings' point clouds.