## 2 Related Work
### Label-Efficient Methods
#### 2.2.2 Label-Efficient Methods for Point Clouds  
Following the impressive results that have been achieved with unsupervised learning in the image analysis, previous efforts to perform unsupervised learning on point clouds have been adaptations of these methods. Several unsupervised methods (e.g., GAN, AE) applied to 3D point clouds are reported in the literature, partly due to the common criticism that a huge amount of labeled data is required for training in a DNN. We provide a quick overview of both types of methods, GANs and AEs.  
**Generative Adversarial Networks (GANs).** Typically, GANs consist of a generator that learns from a latent space to a data distribution of interest. A discriminator distinguishes generated point clouds produced by the generator from the true data distribution. For example, [PERSON] [48] investigated and compared GAN-based methods for generating point clouds in raw-data space and latent space of a pre-trained AE. [PERSON] [49] proposed a \"sandwiching\" reconstruction method that combines a modification of Wasserstein GAN [50] loss with Earth Mover's Distance (EMD). AtlasNet [51] introduces a shape generation framework that represents a 3D shape as a collection of parametric surface elements by locally mapping a set of squares to the target surface of a 3D shape. Although impressive results were achieved, GAN-based methods focus more on generative models of point clouds, which aim to generate point clouds or complete shapes of point clouds.  
**Autoencoders (AEs).** AE is one of the key ingredients in current state-of-the-art unsupervised visual representation learning. An AE is trained to learn a compressed representation by faithfully reconstructing an input original image/point cloud [52]. In FoldingNet [22], the authors adopted the idea of the folding-based decoder to deform a canonical 2D grid onto the underlying 3D object surface of a point cloud, in which the learned representation achieves high linear Support Vector Machines (SVM) classification accuracy on the ModelNet40 dataset. Built on the fully supervised PPFNet [53] and FoldingNet, in PPF-FoldNet [54], the authors improved their earlier solution by involving more features in their network in an unsupervised fashion. PPF-FoldNet achieves better reconstruction performance at rotations and different point densities, but their research focuses on reconstruction rather than downstream tasks. 3D-PointCapsNet [55] employed a dynamic routing scheme to extract discriminative representation while considering the geometric relations between parts, and the extracted representation can lead to improvements in point-cloud classification and part-segmentation tasks. BAE-NET [56] proposed a branched AE network that trains with a collection of objects from the ShapeNetPart dataset trained with a shape co-segmentation task.  
Existing methods achieve state-of-the-art performances in their downstream tasks (i.e., classification, part-segmentation and co-segmentation). However, most of these existing unsupervised AE methods for 3D point clouds are: (1) trained and tested using simple 3D objects; and (2) designed for low-level tasks such as reconstruction, denoising and completion that are not designed for high-level downstream semantic segmentation.  
Overall, learning to automatically generate powerful and robust representations from inhomogeneous point clouds, especially architectural point clouds with complex geometric structures, still poses a challenge. To address these mentioned issues, in this paper, we propose a novel label-efficient DL approach to benefit from the ability of AEs to learn features from unlabeled data, thereby reducing the need of pointwise labels of building point clouds. Moreover, our method improves the encoding capability of FoldingNet [22] by incorporating the local-region information and global-shape information using the EdgeConv layers, allowing unsupervised-learning methods to perform high-level downstream semantic segmentation tasks on point clouds of complex buildings.