Point Of Retreat Epub Download 28

[Gordon Neal]

<div>In the conservation of cultural heritage in different categories, the use of point cloud data for visualization and statistical analysis has become an important heritage evaluation method and trend [15]. Point cloud model rendering methods and techniques have been used for visualization of heritage sites [16]. Multi-modal presentation and the advancing computer graphics display technologies have been applied for the representation and interpretation of archaeological evidence [17, 18]. More integrated digital approaches such as artificial intelligence have been used to analyze point clouds and to monitor heritage deformation [19, 20]. The 3D visualization of laser scanning models has also been increasingly used for community promotion and communication [21]. Point cloud technology is not only an important means of mapping and recording heritage, more and more studies aim to deepen the understanding of heritage through the interpretation of data characteristics. However, these important applications and progress have not yet been reflected in the conservation of Chinese garden rockeries.</div><div></div><div></div><div>The overarching aim of this research is to make full use of 3D information to explore an innovative approach for digital characterization of rockery heritage in Chinese classical gardens. We hope to achieve this goal by answering the following three research questions: (1) How to build 3D models to simulate the surface features of the rockery heritage? (2) What are the main objects and corresponding technical workflows of rockery point cloud visualization? (3) Compared with traditional approaches, what are the innovations and breakthroughs of methods based on point cloud visualization?</div><div></div><div></div><div></div><div></div><div></div><div>point of retreat epub download 28</div><div></div><div>Download File: https://t.co/UDI64kDFmN </div><div></div><div></div><div>In terms of restoration and reconstruction, it is impossible to return a damaged rockery to a known earlier state, because we cannot find two natural stones with the same appearance. The main feature of rockery restorations lies in the inseparability of craft and art in practice, and the restoration of the rockery must lie in the continuation of its characteristics and the inheritance of engineering skills [23]. In the history of Chinese garden protection, in order to better continue the garden culture and tradition, almost all rockery remains have undergone different degrees of restoration or reconstruction, but the results are mixed because of a lack of reliable characterization and evaluation standards [22, 23]. The focus of the analysis is whether the new part continues the characteristics of the historic relics. From this point of view, the appearance features of rockeries are composed of a large number of shape details, and traditional methods based on limited feature points cannot provide sufficient data to support corresponding analysis. At present, the international guidelines barely address the practice-related issues and technicalities [24]. There are many studies on the principles of restoration and reconstruction, but the research on detailed methods and evaluation indicators is very limited [25].</div><div></div><div></div><div>Based on the advantages of laser scanning and point cloud visualization in shape details, we proposed a new method for rockery surface texture analysis. The conventional feature point analysis workflow has been replaced by the identification of spatial patterns through examining the digital model as a whole. We propose a new method of description and analysis, the Point Cloud Visualization Approach (PCVA). The PCVA consists of five steps (Fig. 1):</div><div></div><div></div><div>In this study, we used laser scanning tools to collect spatial information of SRMR. A Leica BLK360 rack-mounted laser scanner and a GeoSLAM ZEB-REVO handheld laser scanner were combined to ensure the accuracy and the integrity of data in confined rockery spaces [30]. The data acquisition process demonstrated that the handheld laser scanner has higher flexibility to cover the extremely complex surfaces of the rockery. The point cloud data was then imported into the CloudCompare 2.12.2 program after registration, combination and clean up in Leica and GeoSLAM preprocessing programs. Outliers were then removed and noise reduction was conducted by point cloud filtering, and the points of plants in the model were separated using the CANUPO plug-in in CloudCompare. Further data processing was conducted to manually refine the classification results. Based on the processed point cloud model, a mesh model was then created using Artec 3D Geomagic Wrap 2017 software for further analysis (Fig. 5).</div><div></div><div></div><div>Based on data integrity, restoration process, and the characteristics, we selected two sample surfaces on the digital models for quantitative analysis and comparison. Sample A is a key part of the historic relic peak that is believed to have been built in the eighteenth-nineteenth century and reflects the characteristic of the "small stone mosaic, the well-proportioned density and space" of SRMR. Sample B is the key part of the restoration completed in 1989. The analysis and comparison of the two samples were performed using both the point cloud data and mesh model data. From the perspective of the overall structure of the rockery, sample A is the main part, and sample B is the subordinate part. While there are obvious differences in their shapes, what we want to examine is whether their surface textures are similar, that is, whether the recently restored part inherits the historical characteristics of ancient remains, and whether the characteristic can be expressed by quantitative means.</div><div></div><div></div><div>The areas and volumes of the point cloud model in the cell space were captured by CloudCompare. In terms of the technical parameters, the volume was calculated by dividing the bottom surface of the point cloud into discrete grids with side length of 0.02 m. The volume of each grid was then calculated and summed up. The calculation formula of surface complexity, k, is as follows:</div><div></div><div></div><div></div><div></div><div></div><div></div><div>Based on the identification of surface complexity, we hope to use the contour curvature to analyze the pattern and similarity of the surface texture, and then evaluate the characteristic of well-proportion. The contours of the digital model were extracted and the box-counting dimension method from Fractal Theory was used to analyze the self-similarity of the contours, so as to capture the unity and consistent of density and space [31, 32]. The contour lines were extracted in CloudCompare. In terms of the technical parameters, some ten 0.01 m-thick point cloud segments were intercepted at 0.2 m spacing in sample A and B (Fig. 7). The maximum edge length of 0.25 m was used as it can retain most of the transitions.</div><div></div><div></div><div>The attributes of shape variation and the interweaving of lightness and darkness were examined to analyze the characteristic of the proper contrast between solid and void. Rockery is an art in which solid and void are integrated in the same space. We first analyzed the composition of solid and void elements on the rockery surface by identifying the degree of undulation and variation of the surface shape. The verticality of points in the digital model was calculated in CloudComplare with a radius of 1 m. The distribution trend of the verticality v of the point cloud model was used to quantify the richness of shape variation. By importing the point cloud verticality data into the MathWorks Matlab R2021a software to calculate the normalized standard deviation of point z values with verticality above 0.75, the shape variation richness index, r, was calculated by using the following formula:</div><div></div><div></div><div>In terms of the shape variation, the results show that the richness index of shape variation of sample A is 0.214 and sample B is 0.143. The result in sample B is 66.8% of that in sample A. According to the histogram of the verticality distribution of the point cloud, the points with higher verticality in the restored part model are concentrated in the middle section, while the points with same attributes in the historic relic peak show a tendency to gather at the top and the middle sections (Fig. 10). We used the Microsoft Excel 2016 software to count the points whose verticality is above 0.75, and use the polynomial fitting tools to identify the trend of the two sample areas. The trend lines of both samples have peaks, but there are two peaks in sample A and sample B has only one (Fig. 10). The calculation results demonstrate that the point clouds in both sample A and B have a tendency to gather and distribute vertically, which means their surfaces have a high degree of undulation. However, the differences in distribution pattern and trend line peaks proves that that the shape fluctuation of sample A is more abundant and has more layers than sample B.</div><div></div><div></div><div>Shape variation analysis: a, b Elevation view of verticality distribution of point cloud model of sample A and B; c, d Histogram of verticality distribution of sample A and B; e, f Histogram of verticality distribution of point cloud above 0.75 in sample A and B</div><div></div><div></div><div>This study establishes an innovative method for the description, analysis and evaluation of the surface texture of rockeries in Chinese classical gardens. This method is based on a high-precision 3D point cloud model obtained by laser scanning technology. The significant characteristics of the case study rockery, including the well-proportioned density and space, and the proper contrast between solid and void, were analyzed through examining the four attributes of surface complexity, contour curvature, shape variation, and the interweaving of lightness and darkness. We designed and implemented the PCVA including multiple algorithms and technical parameters, which can quantitatively calculate the indicators of each attribute. The primary innovation of the PCVA is that it breaks through the analysis logic based on limited feature point data in previous analyses, directly exploring the pattern of shape changes from the big data of the point cloud model. The PCVA makes full use of the information of digital twin technology and point cloud visualization. The advantage of a rich dataset provides new perspectives and systematic frameworks for the efficient analysis of the garden rockery and other irregular heritage components.</div><div></div><div> dd2b598166</div>