CEIG2021

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https://diglib.eg.org/handle/10.2312/2633085

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Now showing 1 - 11 of 11
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    Aplicación del motor de videojuegos Unity para la reconstrucción virtual de yacimientos arqueológicos
    (The Eurographics Association, 2021) Calzado-Martínez, Alberto; García-Fernández, Ángel Luis; Ortega-Alvarado, Lidia M.; Ortega, Lidia M. and Chica, Antonio
    En este trabajo se presenta una aplicación desarrollada para enriquecer y ampliar las técnicas actuales de registro arqueológico. Basada en una arquitectura cliente-servidor, se ha utilizado el motor de videojuegos Unity para implementar una aplicación cliente sencilla e intuitiva que permite realizar la reconstrucción virtual de un yacimiento a partir del escaneado 3D in situ del terreno excavado, así como del escaneado 3D en laboratorio de los hallazgos más importantes. Así se consigue preservar la información espacial del yacimiento, y se facilita la visita virtual del mismo desde cualquier equipo conectado a Internet.
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    CEIG 2021: Frontmatter
    (Eurographics Association, 2021) Ortega, Lidia M.; Chica, Antonio; Ortega, Lidia M. and Chica, Antonio
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    Comparison of GPU-based Methods for Handling Point Cloud Occlusion
    (The Eurographics Association, 2021) López, Alfonso; Jurado, Juan Manuel; Padrón, Emilio José; Ogayar, Carlos Javier; Feito, Francisco Ramón; Ortega, Lidia M. and Chica, Antonio
    Three-dimensional point clouds have conventionally been used along with several sources of information. This fusion can be performed by projecting the point cloud into the image plane and retrieving additional data for each point. Nevertheless, the raw projection omits the occlusion caused by foreground surfaces, thus assigning wrong information to 3D points. For large point clouds, testing the occlusion of each point from every viewpoint is a time-consuming task. Hence, we propose several algorithms implemented in GPU and based on the use of z-buffers. Given the size of nowadays point clouds, we also adapt our methodologies to commodity hardware by splitting the point cloud into several chunks. Finally, we compare their performance through the response time.
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    Digital Layered Models of Architecture and Mural Paintings over Time
    (The Eurographics Association, 2021) Guardia, Milagros; Pogliani, Paola; Bordi, Giulia; Charalambous, Panayiotis; Andujar, Carlos; Munoz-Pandiella, Imanol; Pueyo, Xavier; Ortega, Lidia M. and Chica, Antonio
    The European project Enhancement of Heritage Experiences: The Middle Ages. Digital Layered Models of Architecture and Mural Paintings over Time (EHEM) aims to obtain virtual reconstructions of medieval artistic heritage -architecture with mural paintings- that are as close as possible to the original at different times, incorporating historical-artistic knowledge and the diachronic perspective of heritage. The project has also the purpose of incorporating not only how these painted buildings are and how they were, but also what function they had, how they were used and how they were perceived by the different users. EHEM will offer an instrument for researchers, restorers and heritage curators and will “humanize” the heritage proposing to the spectator of the 21st century an experience close to the users of the Middle Ages.
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    Generación de fenómenos naturales mediante la simulación realista de sólidos deformables en Bifrost
    (The Eurographics Association, 2021) Cruz, José; Jurado, Juan Manuel; Jiménez-Pérez, J. Roberto; Ortega, Lidia; Ortega, Lidia M. and Chica, Antonio
    La simulación de fluidos y sólidos deformables ha sido ampliamente estudiada en Informática Gráfica. Existen diferentes soluciones que posibilitan una simulación cada vez más realista en entornos del mundo real. Para ello, los objetos se modelan geométricamente utilizando una malla de partículas. Esto permite la deformación de medios continuos asociando un conjunto de atributos a cada partícula que determinan su comportamiento y el de sus vecinas. En este trabajo se propone una herramienta interdisciplinar con la que generar simulaciones de fenómenos naturales como avalanchas o inundaciones. Gracias a este tipo de simulaciones físicamente realistas se consigue una manera efectiva de predecir y evaluar con un alto nivel de detalle el impacto producido por distintos tipos de desastres naturales.
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    Generation Process of Intrinsic Images Dataset Through Physically-based Rendering
    (The Eurographics Association, 2021) Rodríguez, Ignacio Moral; López, Alfonso; Jiménez-Perez, J. Roberto; Feito, Francisco R.; Ortega, Lidia; Jurado, Juan M.; Ortega, Lidia M. and Chica, Antonio
    El problema denominado Intrinsic Image Decomposition sigue siendo un desafío por resolver en informática gráfica. Aunque el uso de arquitecturas de aprendizaje profundo supondría un avance significativo, los conjuntos de datos de entrenamiento utilizados son aún reducidos. En este estudio se presenta una metodología para la generación de imágenes y su descomposición en varios canales haciendo uso del motor de renderizado Mitsuba2. Para ello, se ha modelado un escenario natural en el que coexisten distintos tipos de vegetación sobre un terreno. En torno a este escenario, se define una trayectoria sobre la que orbita la cámara para generar un conjunto de imágenes desde distintos puntos de vista de forma automática. Como resultado, se proporcionan conjuntos de datos obtenidos a partir de entornos naturales sintéticos formados por las siguientes capas para cada imagen: mapa de normales, iluminación, albedo y mapa de profundidad. Este desarrollo supone un punto de partida para el estudio del cálculo de la iluminación en entornos reales complejos mediante enfoques basados en aprendizaje profundo.
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    A GPU-accelerated LiDAR Sensor for Generating Labelled Datasets
    (The Eurographics Association, 2021) López, Alfonso; Anguita, Carlos Javier Ogayar; Higueruela, Francisco Ramón Feito; Ortega, Lidia M. and Chica, Antonio
    This paper presents a GPU-based LiDAR simulator to generate large datasets of ground-truth point clouds. LiDAR technology has significantly increased its impact on academic and industrial environments. However, some of its applications require a large amount of annotated LiDAR data. Furthermore, there exist many types of LiDAR sensors. Therefore, developing a parametric LiDAR model allows simulating a wide range of LiDAR scanning technologies and obtaining a significant number of points clouds at no cost. Beyond their intensity data, these synthetic point clouds can be classified with any level of detail.
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    Intensity-Guided Exposure Correction for Indoor LiDAR Scans
    (The Eurographics Association, 2021) Comino Trinidad, Marc; Andújar, Carlos; Bosch, Carles; Chica, Antonio; Munoz-Pandiella, Imanol; Ortega, Lidia M. and Chica, Antonio
    Terrestrial Laser Scanners, also known as LiDAR, are often equipped with color cameras so that both infrared and RGB values are measured for each point sample. High-end scanners also provide panoramic High Dynamic Range (HDR) images. Rendering such HDR colors on conventional displays requires a tone-mapping operator, and getting a suitable exposure everywhere on the image can be challenging for 360° indoor scenes with a variety of rooms and illumination sources. In this paper we present a simple-to-implement tone mapping algorithm for HDR panoramas captured by LiDAR equipment. The key idea is to choose, on a per-pixel basis, an exposure correction factor based on the local intensity (infrared reflectivity). Since LiDAR intensity values for indoor scenes are nearly independent from the external illumination, we show that intensity-guided exposure correction often outperforms state-of-the-art tone-mapping operators on this kind of scenes.
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    ISSIGraph: An Open Source Multi-platform C++ Tool for Rapid 2D/3D Wireframe Sketching
    (The Eurographics Association, 2021) Jiménez de Parga, Carlos; ; Ortega, Lidia M. and Chica, Antonio
    Rapid sketch modelling and Computer-Aided Design (CAD) initiation are increasingly demanded activities in the creative and educational areas. For this reason, this paper presents a cross-platform C++ toolkit with the aim to facilitate the illustration of technical concepts in a fast way using basic quadric objects, Bézier and NURBS surfaces with a wireframe representation. This tool was designed using Software Engineering principles guided by a basic Rational Unified Process (RUP) methodology with the application of the Unified Modelling Language (UML). This tool perfectly works in all computers with very elemental 3D graphics hardware and with OpenGL support. The resulting benchmarks demonstrate that ISSIGraph has a very small CPU footprint that make it suitable for any platform. As a consequence, this application is well suited for rapid 2D/3D project sketching in the creative and engineering fields, as well as an initiation to CAD techniques for students and computer fans.
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    Neural Colorization of Laser Scans
    (The Eurographics Association, 2021) Comino Trinidad, Marc; Andujar, Carlos; Bosch, Carles; Chica, Antonio; Muñoz-Pandiella, Imanol; Ortega, Lidia M. and Chica, Antonio
    Laser scanners enable the digitization of 3D surfaces by generating a point cloud where each point sample includes an intensity (infrared reflectivity) value. Some LiDAR scanners also incorporate cameras to capture the color of the surfaces visible from the scanner location. Getting usable colors everywhere across 360° scans is a challenging task, especially for indoor scenes. LiDAR scanners lack flashes, and placing proper light sources for a 360° indoor scene is either unfeasible or undesirable. As a result, color data from LiDAR scans often do not have an adequate quality, either because of poor exposition (too bright or too dark areas) or because of severe illumination changes between scans (e.g. direct Sunlight vs cloudy lighting). In this paper, we present a new method to recover plausible color data from the infrared data available in LiDAR scans. The main idea is to train an adapted image-to-image translation network using color and intensity values on well-exposed areas of scans. At inference time, the network is able to recover plausible color using exclusively the intensity values. The immediate application of our approach is the selective colorization of LiDAR data in those scans or regions with missing or poor color data.
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    Synthetic Data Set Generation for the Evaluation of Image Acquisition Strategies Applied to Deep Learning Based Industrial Component Inspection Systems
    (The Eurographics Association, 2021) Saiz, Fátima A.; Alfaro, Garazi; Barandiaran, Iñigo; Garcia, Sara; Carretero, M. P.; Graña, Manuel; Ortega, Lidia M. and Chica, Antonio
    Automated visual inspection is an ongoing machine vision challenge for industry. Faced with increasingly demanding quality standards it is reasonable to address the transition from a manual inspection system to an automatic one using some advanced machine learning approaches such as deep learning models. However, the introduction of neural models in environments such as the manufacturing industry find certain impairments or limitations. Indeed, due to the harsh conditions of manufacturing environments, there is usually the limitation of collecting a high quality database for training neural models. Also, the imbalance between non-defective and defective samples is very common issue in this type of scenarios. To alleviate these problems, this work proposes a pipeline to generate rendered images from CAD models of industrial components, to subsequently feed an anomaly detection model based on Deep Learning. Our approach can simulate the potential geometric and photometric transformations in which the parts could be presented to a real camera to faithfully reproduce the image acquisition behavior of an automatic inspection system. We evaluated the accuracy of several neural models trained with different synthetically generated data set simulating different transformations such as part temperature or part position and orientation with respect to a given camera. The results shows the feasibility of the proposed approach during the design and evaluation process of the image acquisition setup and to guarantee the success of the real future application.