## 1 Introduction  
Documentation of heritage objects by means of surveying techniques has a long history. Indeed, surveying techniques have been an integral part of any conservation effort as well as archaeological missions since the early days of heritage conservation [1]. The need for geospatial data was, and remains, important in order to present a real and tangible archive. While 3D techniques have been used for at least several decades for this purpose, they have seen a very important development since the beginning of the third millennium. This is due to significant improvements in the quality of 3D recording sensors, including the invention of the laser scanning or LIDAR technique [2]. Fast and accurate heritage recording became feasible, although it remained an expensive endeavor. However,during the last decade, further improvements in both hardware and software have rendered the 3D nature of heritage documentation more and more ubiquitous. The term \"reality-based 3D modeling\" was introduced, which nowadays depend on mainly two methods: passive or image-based and active or range-based [3]. Photogrammetry represents the most commonly used technique in the image-based approach. A branch of science which has a long history in 3D data generation since the advent of aerial photography during the early 20 th century [4], photogrammetry has seen massive improvements in terms of computing capability as well as the results offered. The traditionally surveying-oriented photogrammetric process was augmented by various techniques from the computer vision domain, such as Structure from Motion (SfM) and dense matching, to create a versatile and relatively low-cost solution for 3D heritage recording [3]. Of course, improvements in lens and sensor capabilities as well as the democratisation of drones have also improved photogrammetry's popularity even further amongst the heritage community [5]. As far as the active range-based approach is concerned, the LIDAR technology (including both the Terrestrial Laser Scanning or TLS and the Aerial Laser Scanning or ALS) has also developed tremendously. Using as comparison parameter the scan rate of Time of Flight (ToF) devices produced for example by Trimble, the point per second rate has improved exponentially from 5000 points/second (Trimble GX) in 2005 [6] to 25,000 points/second (Trimble SX10) in 2017 [7] and 100,000 points/second (Trimble X7) in 2020 ([[https://geospatialx7.trimble.com](https://geospatialx7.trimble.com)]([https://geospatialx7.trimble.com](https://geospatialx7.trimble.com)), retrieved 22 January 2020). This is also supported by significant improvements in the software part, with workflow automation taking more and more importance aided by ever more powerful computing cores available for the user [8; 9]. The most common result of the 3D recording process is a 3D point cloud, either via direct laser scanning or the deployment of dense matching algorithms on an oriented photogrammetric network. The point cloud stores geometric information (i.e., XYZ coordinates) which forms a 3D representation of the thence scanned object [10]. Several other pieces of information can also be stored within the point cloud, commonly other geometric features such as point normals, curvatures, linearity, and planarity (relative to a local plane) [11] as well as RGB color or scan intensity in the case of a TLS point cloud. However, this information remains singular for each point within the point cloud. In order to be able to perform more meaningful operations on the point cloud, segmentation must be performed and followed by semantic labeling, thus virtually disassembling the raw point cloud into smaller, classified clusters [12]. The segmented clusters of point clouds can thenceforth be treated as classified point cloud, from whence various analyses, 3D modeling, and model predictions could be performed.