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Now showing 1 - 10 of 24
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    Cylindrical Transform Slicing of Revolute Parts with Overhangs for Laser Metal Deposition
    (The Eurographics Association, 2022) Montoya-Zapata, Diego; Moreno, Aitor; Ortiz, Igor; Ruiz-Salguero, Oscar; Posada, Jorge; Posada, Jorge; Serrano, Ana
    In the context of Laser Metal Deposition (LMD), temporary support structures are needed to manufacture overhanging features. In order to limit the need for supports, multi-axis machines intervene in the deposition by sequentially repositioning the part. Under multi-axis rotations and translations, slicing and toolpath generation represent significant challenges. Slicing has been partially addressed by authors in multi-axis LMD. However, tool-path generation in multi-axis LMD is rarely touched. One of the reasons is that the required slices for LMD may be strongly non-developable. This fact produces a significant mismatch between the tool-path speeds and other parameters in Parametric space vs. actual Euclidean space. For the particular case of developable slices present in workpieces with cylindrical kernel and overhanging neighborhoods, this manuscript presents a methodology for LMD tool path generation. Our algorithm takes advantage of existing cylindrical iso-radial slicing by generating a path in the (?, z) parameter space and isometrically translating it into the R3 Euclidean space. The presented approach is advantageous because it allows the path-planning of complex structures by using the methods for conventional 2.5-axis AM. Our computer experiments show that the presented approach can be effectively used in manufacturing industrial/mechanical pieces (e.g., spur gears). Future work includes the generation of the machine g-code for actual LMD equipment.
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    Modal Locomotion of C. elegans
    (The Eurographics Association, 2019) Mujika, Andoni; Merino, Sara; Leškovský, Peter; Epelde, Gorka; Oyarzun, David; Otaduy, Miguel Angel; Casas, Dan and Jarabo, Adrián
    Caenorhabditis elegans (C. elegans) is a roundworm that, thanks to its combination of biological simplicity and behavioral richness, offers an excellent opportunity for initial experimentation of many human diseases. In this work, we introduce a locomotion model for C. elegans, which can enable in-silico validation of behavioral experiments prior to physical experimentation with actual C. elegans specimens. Our model enables interactive simulation of self-propelling C. elegans, using as sole input biologically inspired muscle forces and frictional contact. The key to our model is a simple locomotion control strategy that activates selected natural vibration modes of the worm. We perform an offline analysis of the natural vibration modes, select those that best match the deformation of the worm during locomotion, and design force profiles that activate these vibration modes in a coordinated manner. Together with force compensation for momentum conservation and an anisotropic friction model, we achieve locomotions that match qualitatively those of real-world worms. Our approach is general, and could be extended to the locomotion of other types of animals or characters.
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    Perfect Spatial Hashing for Point-cloud-to-mesh Registration
    (The Eurographics Association, 2019) Mejia-Parra, Daniel; Lalinde-Pulido, Juan; Sánchez, Jairo R.; Ruiz-Salguero, Oscar; Posada, Jorge; Casas, Dan and Jarabo, Adrián
    Point-cloud-to-mesh registration estimates a rigid transformation that minimizes the distance between a point sample of a surface and a reference mesh of such a surface, both lying in different coordinate systems. Point-cloud-to-mesh-registration is an ubiquitous problem in medical imaging, CAD CAM CAE, reverse engineering, virtual reality and many other disciplines. Common registration methods include Iterative Closest Point (ICP), RANdom SAmple Consensus (RANSAC) and Normal Distribution Transform (NDT). These methods require to repeatedly estimate the distance between a point cloud and a mesh, which becomes computationally expensive as the point set sizes increase. To overcome this problem, this article presents the implementation of a Perfect Spatial Hashing for point-cloud-to-mesh registration. The complexity of the registration algorithm using Perfect Spatial Hashing is O(NYxn) (NY : point cloud size, n: number of max. ICP iterations), compared to standard octrees and kd-trees (time complexity O(NY log(NT)xn), NT : reference mesh size). The cost of pre-processing is O(NT +(N3H )2) (N3H : Hash table size). The test results show convergence of the algorithm (error below 7e-05) for massive point clouds / reference meshes (NY = 50k and NT = 28055k, respectively). Future work includes GPU implementation of the algorithm for fast registration of massive point clouds.
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    Procedural Location of Roads Using Desire Paths
    (The Eurographics Association, 2019) Real, Pablo; Martínez-Gil, Francisco; Martínez-Durá, Rafael J.; García-Fernández, Ignacio; Casas, Dan and Jarabo, Adrián
    Procedural modelling of realistic environments that include elements derived from human activity can largely reduce production cost in animation, video-games and feature films. We address the problem of placing roads and other human-made elements, such as buildings, in a way that is consistent with the scene relief. Our approach is based on the calculation of so called desire paths, by means of the generation of many optimal paths according to different cost or distance functions. Using this idea, we rely on the information of the terrain properties to emulate the exploration of the scenario by a large number of pedestrians. From the routes generated by the pedestrians, we define walkability and habitability maps on the scene that can be later used to locate roads or buildings.
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    Guided Modeling of Natural Scenarios: Vegetation and Terrain
    (The Eurographics Association, 2022) Collado, José Antonio; López, Alfonso; Pérez, Juan Roberto Jiménez; Ortega, Lidia M.; Jurado, Juan M.; Feito, Francisco; Posada, Jorge; Serrano, Ana
    The generation of realistic natural scenarios is a longstanding and ongoing challenge in Computer Graphics. LiDAR (Laser Imaging Detection and Ranging) point clouds have been gaining interest for the representation and analysis of real-world scenarios. However, the output of these sensors is conditioned by several parameters, including, but not limited to, distance to scanning target, aperture angle, number of laser beams, as well as systematic and random errors for the acquisition process. Hence, LiDAR point clouds may present inaccuracies and low density, thus hardening their visualization. In this work, we propose reconstructing the surveyed environments to enhance the point cloud density and provide a 3D representation of the scenario. To this end, ground and vegetation layers are detected and parameterized to allow their reconstruction. As a result, point clouds of any required density can be modeled, as well as 3D realistic natural scenarios that may lead to procedural generation through their parameterization.
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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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    3D Environment Understanding in Real-time Using Input CAD Models for AR Applications
    (The Eurographics Association, 2019) Rodríguez, David Jurado; Rodríguez, Juan Manuel Jurado; Alvarado, Lidia Ortega; Higueruela, Francisco Ramón Feito; Casas, Dan and Jarabo, Adrián
    The automatic recognition of real environments has become a relevant issue for multiple purposes in computer graphics, computer vision and artificial intelligence. In this work, we focus on environment understanding according to input CAD models for an Augmented Reality (AR) application. We provide a novel solution for the management of building infrastructures in indoor spaces. The use case has been the University of Jaén to visualize correctly their service facilites from AR. To this end, firstly, the CAD models (2D) have been segmented in order to simplify its geometry. As a result, an efficient data structure has been created for real-time alignement to scanned data. Secondly, we have developed a mobile application based on ARCore library to capture and generate 3D planes of the user environment. Finally, we have carried out a method to align automatically the virtual elements such as walls, doors and grounds to the real world. The main objective of this research is to calculate the needed geometric transformations of virtual elements and thus, to achieve a correct overlappping with the real world by understanding their physical and spatial constraints in real time.
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    DriveRNN: Predicting Drivers' Attention with Deep Recurrent Networks
    (The Eurographics Association, 2022) Lasheras-Hernandez, Blanca; Masia, Belen; Martin, Daniel; Posada, Jorge; Serrano, Ana
    Lately, the automotive industry has experienced a significant development led by the ambitious objective of creating an autonomous vehicle. This entails understanding driving behaviors in different environments, which usually requires gathering and analyzing large amounts of behavioral data from many drivers. However, this is usually a complex and time-consuming task, and data-driven techniques have proven to be a faster, yet robust alternative to modeling drivers' behavior. In this work, we propose a deep learning approach to address this challenging problem. We resort to a novel convolutional recurrent architecture to learn spatio-temporal features of driving behaviors based on RGB sequences of the environment in front of the vehicle. Our model is able to predict drivers' attention in different scenarios while outperforming competing works by a large margin.
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    View-dependent Hierarchical Rendering of Massive Point Clouds through Textured Splats
    (The Eurographics Association, 2019) Comino Trinidad, Marc; Calaf, Antonio Chica; Gran, Carlos Andújar; Casas, Dan and Jarabo, Adrián
    Nowadays, there are multiple available range scanning technologies which can capture extremely detailed models of realworld surfaces. The result of such process is usually a set of point clouds which can contain billions of points. While these point clouds can be used and processed offline for a variety of purposes (such as surface reconstruction and offline rendering) it is unfeasible to interactively visualize the raw point data. The most common approach is to use a hierarchical representation to render varying-size oriented splats, but this method also has its limitations as usually a single color is encoded for each point sample. Some authors have proposed the use of color-textured splats, but these either have been designed for offline rendering or do not address the efficient encoding of image datasets into textures. In this work, we propose extending point clouds by encoding their color information into textures and using a pruning and scaling rendering algorithm to achieve interactive rendering. Our approach can be combined with hierarchical point-based representations to allow for real-time rendering of massive point clouds in commodity hardware.
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    Sensitivity Analysis in Shape Optimization using Voxel Density Penalization
    (The Eurographics Association, 2019) Montoya-Zapata, Diego; Acosta, Diego A.; Moreno, Aitor; Posada, Jorge; Ruiz-Salguero, Oscar; Casas, Dan and Jarabo, Adrián
    Shape optimization in the context of technical design is the process by which mechanical demands (e.g. loads, stresses) govern a sequence of piece instances, which satisfy the demands, while at the same time evolving towards more attractive geometric features (e.g. lighter, cheaper, etc.). The SIMP (Solid Isotropic Material with Penalization) strategy seeks a redistribution of local densities of a part in order to stand stress / strain demands. Neighborhoods (e.g. voxels) whose density drifts to lower values are considered superfluous and removed, leading to an optimization of the part shape. This manuscript presents a study on how the parameters governing the voxel pruning affect the convergence speed and performance of the attained shape. A stronger penalization factor establishes the criteria by which thin voxels are considered void. In addition, the filter discourages punctured, chessboard pattern regions. The SIMP algorithm produces a forecasted density map on the whole piece voxels. A post-processing is applied to effectively eliminate voxels with low density, to obtain the effective shape. In the literature, mechanical performance finite element analyses are conducted on the full voxel set with diluted densities by linearly weakening each voxel resistance according to its diluted density. Numerical tests show that this approach predicts a more favorable mechanical performance as compared with the one obtained with the shape which actually lacks the voxels with low density. This voxel density - based optimization is particularly convenient for additive manufacturing, as shown with the piece actually produced in this work. Future endeavors include different evolution processes, albeit based on variable density voxel sets, and mechanical tests conducted on the actual sample produced by additive manufacture.