## 1 Introduction  
To understand the semantic segmentation of point clouds in the 3D domain, it is helpful to first understand the origins of this technique in the 2D domain. Semantic segmentation, in the first place, is a computer vision technique that involves partitioning an image into multiple semantic regions or segments, where each segment corresponds to a specific region of interest within the image. The goal of semantic segmentation is to classify and assign a label to every pixel in an image, indicating the category or class it belongs to. Unlike other types of image segmentation, such as instance segmentation or boundary detection, semantic segmentation focuses on understanding the high-level content of an image rather than individual objects or their boundaries. It aims to capture the meaning or semantics of the image by assigning a meaningful label to each pixel. Before the introduction of machine learning, early adopters of semantic segmentation in images relied on traditional computer vision techniques. They extracted low-level features like color, texture, edges, and gradients from the images. Various segmentation algorithms were then applied to partition the image into regions based on these features. Techniques such as thresholding, region growing, and edge-based segmentation were commonly used [1]. This process aimed to identify distinct regions or boundaries within the image for further analysis and classification. However, these approaches were limited by the need for handcrafted features and explicit rules, making them less flexible and robust compared to machine-learning-based methods. Machine learning models, such as deep neural networks, have since emerged as powerful tools that can automatically learn and extract complex features from images, significantly improving the accuracy and generalizability of semantic segmentation. The development of convolutional deep neural networks alleviated the need for engineered features and produced a powerful representation that captures texture, shape, and contextual information [2]. The introduction of fully convolutional networks (FCNs) to semantic segmentation tasks alleviated the need for preprocessing images into low-resolution superpixels and was end-to-end trained to predict a semantic label for each pixel in an image [3]. The FCN was able to achieve state-of-the-art performance on the PASCAL VOC 2012 dataset [4] and was the first model to surpass the performance of traditional computer vision techniques. Similarly, the semantic segmentation of point cloud data refers to the process of classifying individual points in three-dimensional space into semantic categories or labels. It involves assigning a meaningful label to each point based on its characteristics and the context of the surrounding points. The objective is to segment the point cloud into different regions or objects, as shown in Figure 1, where each region or object is associated with a specific semantic class. Deep-learning-based segmentation for point clouds opens up new opportunities for applications, such as robotics, industrial automation, and 3D scene understanding also for the construction industry, enabling machines to perceive and interact with the environment in three-dimensional space. Among various scene understanding problems, 3D semantic segmentation allows for finding accurate object boundaries along with their labels in 3D space, which is useful for fine-grained tasks [5]. The architecture, engineering, and construction (AEC) sector ranks as one of the most intensive fields where vision-based systems are used to facilitate decision-making processes during the construction phase. Construction sites make efficient monitoring extremely tedious and difficult due to clutter and disorder. Due to their intrinsic nature, job sites are prone to management failures such as unsatisfactory construction quality, delays on schedule, extra costs, and even injuries [6].