Restoration of paintings on domes with non-developable geometry (Los Santos Juanes Church in Valencia)

[EN] The restoration of paintings on elements in cultural heritage buildings (fundamentally, churches) involves two structural problems: capturing the geometry of the construction element and its development. In many cases, the geometries are regular (e.g., cylinders, spheres, elliptical domes). However, there are cases in which the elements cannot be adapted to any known geometry, much less one that can be mathematically developed. The development of surfaces becomes essential for the restoration of paintings over "flat elements" (over which work is performed on the ground) that are subsequently transferred to the real surface (ceilings). The mathematical transformations that allow regular geometries to be developed are widely known (cartographic projections). However, when the geometry is irregular, there is no development. This study presents a new methodology based on differential rectification and its application for the development of oculi in the Los Santos Juanes Church (Valencia), whose geometry is completely irregular both in shape and as a result of construction defects (and damage caused by fire). The present study focuses on the restoration of paintings damaged by fire.Navarro Esteve, PJ.; Yudici Oliver, SA.; Herráez Boquera, J.; Denia Rios, JL.; Martín Sánchez, MT.; Rodríguez Pereña, J. (2018). Restoration of paintings on domes with non-developable geometry (Los Santos Juanes Church in Valencia). International Journal of Architectural Heritage. 12(2):169-177. https://doi.org/10.1080/15583058.2017.1356946S16917712

Optimal modelling of buildings through simultaneous automatic simplifications of point clouds obtained with a laser scanner

[EN] In recent years, the laser scanner has become the most used tool for modelling buildings in pure documentation and structural studies. Laser scanning provides large numbers of points in a minimum amount of time with great precision. The point clouds generated and the subsequent mosaics (data fusion of different clouds) contain millions of points with a heterogeneous density that define the 3D geometry of the buildings. Often, the number of points results in excessive information without offering a better definition. As a result, it is necessary to analyse which points can be eliminated and which ones cannot, based on precision criteria, to obtain a precise geometry with the smallest possible number of points for each part of the building. The algorithm developed in this work reduces the point clouds (in mosaics made up of clouds with over 10 million points) with precision criteria by as much as 99% while still accurately resolving the geometry of the object. The developed process is automatic such that different models with different resolutions can be obtained simultaneously. As a result, we obtain single clouds with homogenous distributions and densities throughout the model of the building (based on multiple overlapping clouds), with a computational cost of only a few seconds per cloud. The final result is a complete model of the entire building with the optimal resolution for each element of the structure. (C) 2016 Elsevier Ltd. All rights reserved.S2432519

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on epsilon insensitive support vector regression (epsilon-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of epsilon-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on epsilon insensitive support vector regression (epsilon-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of epsilon-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

Feature Sensitive Three-Dimensional Point Cloud Simplification using Support Vector Regression

Contemporary three-dimensional (3D) scanning devices are characterized by high speed and resolution. They provide dense point clouds that contain abundant data about scanned objects and require computationally intensive and time consuming processing. On the other hand, point clouds usually contain a large amount of redundant data that carry little or no additional information about scanned object geometry. To facilitate further analysis and extraction of relevant information from point cloud, as well as faster transfer of data between different computational devices, it is rational to carry out its simplification at an early stage of the processing. However, the reduction of data during simplification has to ensure high level of information contents preservation; simplification has to be feature sensitive. In this paper we propose a method for feature sensitive simplification of 3D point clouds that is based on ε insensitive support vector regression (ε-SVR). The proposed method is intended for structured point clouds. It exploits the flatness property of ε-SVR for effective recognition of points in high curvature areas of scanned lines. The points from these areas are kept in simplified point cloud along with a reduced number of points from flat areas. In addition, the proposed method effectively detects the points in the vicinity of sharp edges without additional processing. Proposed simplification method is experimentally verified using three real world case studies. To estimate the quality of the simplification, we employ non-uniform rational b-splines fitting to initial and reduced scan lines

Consistent Density Scanning and Information Extraction From Point Clouds of Building Interiors

Over the last decade, 3D range scanning systems have improved considerably enabling the designers to capture large and complex domains such as building interiors. The captured point cloud is processed to extract specific Building Information Models, where the main research challenge is to simultaneously handle huge and cohesive point clouds representing multiple objects, occluded features and vast geometric diversity. These domain characteristics increase the data complexities and thus make it difficult to extract accurate information models from the captured point clouds. The research work presented in this thesis improves the information extraction pipeline with the development of novel algorithms for consistent density scanning and information extraction automation for building interiors. A restricted density-based, scan planning methodology computes the number of scans to cover large linear domains while ensuring desired data density and reducing rigorous post-processing of data sets. The research work further develops effective algorithms to transform the captured data into information models in terms of domain features (layouts), meaningful data clusters (segmented data) and specific shape attributes (occluded boundaries) having better practical utility. Initially, a direct point-based simplification and layout extraction algorithm is presented that can handle the cohesive point clouds by adaptive simplification and an accurate layout extraction approach without generating an intermediate model. Further, three information extraction algorithms are presented that transforms point clouds into meaningful clusters. The novelty of these algorithms lies in the fact that they work directly on point clouds by exploiting their inherent characteristic. First a rapid data clustering algorithm is presented to quickly identify objects in the scanned scene using a robust hue, saturation and value (H S V) color model for better scene understanding. A hierarchical clustering algorithm is developed to handle the vast geometric diversity ranging from planar walls to complex freeform objects. The shape adaptive parameters help to segment planar as well as complex interiors whereas combining color and geometry based segmentation criterion improves clustering reliability and identifies unique clusters from geometrically similar regions. Finally, a progressive scan line based, side-ratio constraint algorithm is presented to identify occluded boundary data points by investigating their spatial discontinuity