###### Abstract
Another post-processing method based on normalized vector field was introduced and applied in operations such as surface smoothing, details enhancement, and artificial coloring of the 3D scanned surface [6]. Considering a smooth base normal, a relief extraction technique that estimates the height distribution of reliefs and background by integrating relative heights between adjacent vertices was presented by [PERSON] et al. [8]. The normals of the base surface were first estimated to determine the height of the surface points and a Gaussian mixture model was applied to the height distribution to obtain a threshold that was used to decide if a surface point is a relief or background. Degraded regions were identified and removed using a filter that relies on the size and shape of the identified regions. This technique was demonstrated to be reliable in applications such as detail exaggeration and dampening, cut and paste of relief, and drawing of prominent curves. Recently, a digital 3D stone rubbing approach which also estimates the heights of mesh vertices using a weighted least square method was presented by [PERSON] et al. [15]. The estimated heights were transformed subsequently to grey values using a nonlinear transformation function. The transformed heights were segmented into black, white, or grey colors based on the relationship between the individual height and the mean and standard deviation of the heights of neighboring vertices. In this manner, a digital stone rubbing similar to the traditional rubbing was determined from the 3D mesh. Following the successful application of the Frangi filter in the detection of blood vesselness [17], a modified Frangi filter was developed to extract relief details from the surface of 3D mesh [7; 18]. Considering the canal-like shape of the reliefs, curvatures are intuitive and effective measures for detecting reliefs. At a position corresponding to the relief, its minimum and maximum curvatures, \(\kappa_{min}\) and \(\kappa_{max}\), would be close to zero and a large value, respectively. The earlier modification of the Frangi filter is based on the computation of curvature to obtain carvings. This is achieved by clustering the surface regions and using a measure of saliency to rank the detected carvings on weathered plasters [7]. In addition, the authors presented a flexible and interactive tool with simple visualization for modifying reliefs. While this method is not suitable for extracting reliefs from the rough surfaces of steel because the presence of degraded regions disrupt the ranking of the saliency, another variants of the Frangi filter handles this problem by applying two vesselness values of different curvature sizes and selecting an acceptable threshold to effectively remove the irregular surface regions [18]. A handful of machine learning techniques have been introduced lately to analyze prehistoric cultural heritage [19; 20; 21]. The features extracted from these monuments can include the textural information of the soil samples of the monuments or the digital contents of the 2D/3D images that are captured by electronic cameras and scanners. [PERSON] et al. [22] presented a supervised machine learning method to refine bas-relief surfaces for reversed engineering. Initially, the surface of the bas-relief was segmented using a snake-based method and the background region was estimated using b-spline surface fitting curves. Although the results obtained are promising, the roughness nature of the surface of the stele affects the performance of this method. In addition, the method considered only the relief and background labels, and ignored the degraded surface region. The use of the two-class labels affects the performance of these models since the degraded surface region share geometrical features similar to the reliefs, and this interferes in the conditions set for extracting reliefs [23]. More recently, support vector machine (SVM) was applied to extract relief region from the surface of stone monument [24].