The conservation of architectural heritage is a crucial task for preserving the history and cultural value of our cities. This work proposes a methodology that combines advanced techniques of multisensory data acquisition and analysis to generate labeled datasets of anomalous zones in historic buildings. The proposed method includes the use of artificial intelligence techniques, such as the Segment Anything Model (SAM), for image labeling, ensuring high precision through the involvement of conservation experts. Furthermore, due to the limited number of images focusing on specific anomalies, we address this issue by employing the Gaussian Splatting algorithm, which enables the reconstruction of 3D scenes and the generation of synthetic images centered on the anomalies of interest. These synthetic images are automatically labeled, resulting in a high-quality dataset suitable for training neural networks that support preventive conservation strategies in architectural heritage. By tackling the critical challenge of insufficient labeled data, this approach improves the robustness and reliability of machine learning models for anomaly detection in architectural structures.

Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D datasets of real-world trees and generating increasingly plausible branching structures. The wide availability of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. The most relevant contributions are summarized and classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, and (3) image-based modeling. In addition, we describe other approaches focused on the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.

This work describes an efficient approach for generating large 3D thermal point clouds considering the occlusion of camera viewpoints. For that purpose, RGB and thermal imagery are first corrected and fused with an intensity correlation-based algorithm. Then, absolute temperature values are obtained from the normalized data. Finally, thermal imagery is mapped on the point cloud using the Graphics Processing Unit (GPU) hardware. The proposed occlusion-aware mapping algorithm is massively parallelized using OpenGL's compute shaders. Our solution allows generating dense thermal point clouds in a lower response time compared with other notable soft-ware solutions (e.g., Agisoft Metashape or Pix4Dmapper) that yield results with a significantly lower point density.

Modelling of material appearance from reflectance measurements has become increasingly prevalent due to the development of novel methodologies in Computer Graphics. In the last few years, some advances have been made in measuring the light-material interactions, by employing goniometers/reflectometers under specific laboratory’s constraints. A wide range of applications benefit from data-driven appearance modelling techniques and material databases to create photorealistic scenarios and physically based simulations. However, important limitations arise from the current material scanning process, mostly related to the high diversity of existing materials in the real-world, the tedious process for material scanning and the spectral characterisation behaviour. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach for material appearance acquisition using hyperspectral data is proposed. A dense 3D point cloud filled with spectral data was generated from the images obtained by an unmanned aerial vehicle (UAV) equipped with an RGB camera and a hyperspectral sensor. The observed hyperspectral signatures were used to recognise natural and artificial materials in the 3D point cloud according to spectral similarity. Then, a parametrisation of Bidirectional Reflectance Distribution Function (BRDF) was carried out by sampling the BRDF space for each material. Consequently, each material is characterised by multiple samples with different incoming and outgoing angles. Finally, an analysis of BRDF sample completeness is performed considering four sunlight positions and 16x16 resolution for each material. The results demonstrated the capability of the used technology and the effectiveness of our method to be used in applications such as spectral rendering and real-word material acquisition and classification.

In this work, we present an efficient graphics processing unit (GPU)-based light detection and ranging (LiDAR) scanner simulator. Laser-based scanning is a useful tool for applications ranging from reverse engineering or quality control at an object scale to large-scale environmental monitoring or topographic mapping. Beyond that, other specific applications require a large amount of LiDAR data during development, such as autonomous driving. Unfortunately, it is not easy to get a sufficient amount of ground-truth data due to time constraints and available resources. However, LiDAR simulation can generate classified data at a reduced cost. We propose a parameterized LiDAR to emulate a wide range of sensor models from airborne to terrestrial scanning. OpenGL’s compute shaders are used to massively generate beams emitted by the virtual LiDAR sensors and solve their collision with the surrounding environment even with multiple returns. Our work is mainly intended for the rapid generation of datasets for neural networks, consisting of hundreds of millions of points. The conducted tests show that the proposed approach outperforms a sequential LiDAR scanning. Its capabilities for generating huge labeled datasets have also been shown to improve previous studies.

This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework.

Real-time visualization in web-based system remains challenging due to the amount of information associated to a 3D urban models. However, these 3D models are not able to provide advanced management of urban infrastructures, such as underground facilities. Nowadays, 3D GIS is considered the appropriate tool to provide accurate analysis and decision support based on spatial data. This paper presents a web-GIS application for 3D visualization, navigation, interaction and analysis of underground infrastructures through virtual reality. The growth of underground cities is a complex problem without easy solutions. In general, these infrastructures cannot be directly visualized. Thus, subsoil mapping can help us to develop a clearer representation of underground's pipes, cables or water mains. In addition, the approach of virtual reality provides an immersive experience and novelty interaction to acquire a complete knowledge about underground city structures. Experimental results show an integral application for the efficient management of underground infrastructure in real-time.

This work describes the EL-REP, a new 2D decomposition scheme with interesting properties and applications. The EL-REP can be computed for one or more simple polygons of any kind: convex or nonconvex, with or without holes and even with several shells. A method for constructing this decomposition is described in detail, together with several of its main applications: fast point-in-polygon inclusion test, 2D location, triangulation of polygons, and collision detection.

Abstract In this work we present new point inclusion algorithms for non‐convex polygons. These algorithms do not perform any pre‐processing or any type of decomposition nor features classification, which makes them especially suitable for deformable or moving polygons. The algorithms are more accurate and robust than others in the sense that they consider the inclusion of the point in the vertices and edges of the polygon, and deal with the special cases correctly. In order to perform this inclusion test efficiently, they use the sign of the barycentric coordinates of the test point with regard to the triangles formed by the edges and an origin that depends on the test point. This set of triangles, which is a special simplicial covering of the polygon, is constructed after a transformation of the polygon that simplifies the calculations involved in the inclusion test. Then, an appropriate ordering of the rejection tests allows us to optimize this method. Our algorithms have been tested for robustness and compared with ray‐crossing methods, showing a significant improvement.

Abstract Collision detection (CD) among complex objects in motion is an open question because of its algorithmic complexity. In this paper, we present a CD algorithm between a particle and a complex rigid solid. In order to represent solids, we use a simplicial covering scheme by means of 3‐simplices. With this representation system, complex polyhedra and closed triangle meshes can be represented and used in CD with a particle. A particle can be represented by a point, where the real dimensions of the particle are not important. This CD algorithm has been extended for a particle represented by a sphere which in turn represents an approximation to the particle dimensions. In order to efficiently classify the particles and the simplices of the polyhedra covering, we use a new hierarchical data structure named tetra‐tree that decomposes the space into tetra‐cones. These algorithms also use bounding volumes and temporal and geometric coherence, as well as incremental calculations in order to determine the collision in an efficient and exact way. Because of the use of sign operations with signed volumes and barycentric coordinates, we conclude that operations carried out using this method are more robust and efficient than those used in classic algorithms.

The calculation of the minimum cost path is one of the newest tools in the spatial analysis of data. The problem acquires an added complexity when the cost associated to a phenomenon not only depends on its intensity but also on the direction in which it is revealed in the space. In this case, the authors run into an anisotropic phenomenon where the friction associated with it is also anisotropic. The article describes a methodology for the design of cost functions as well as anisotropic friction functions, which are based on semispatial statistical items as they are the circular variables.