Search

Your search keyword '"Jurado, Juan M."' showing total 28 results
Start Over Author "Jurado, Juan M."
28 results on '"Jurado, Juan M."'

11. High Precision Geomatic Tools for Improving Harvest of Olive Grove in Advance

Author

Ramos, Ma Isabel, Jurado, Juan M., Cárdenas, José L., Enríquez, Carlos, Ortega, Lidia, Martínez, Ma Lourdes, Feito, Francisco R., Cubillas, Juan J., Monteiro, Jânio, editor, João Silva, António, editor, Mortal, António, editor, Aníbal, Jaime, editor, Moreira da Silva, Manuela, editor, Oliveira, Miguel, editor, and Sousa, Nelson, editor

Published
2020
Full Text
View/download PDF

17. Modeling of the 3D Tree Skeleton Using Real-World Data: A Survey

Author

Cardenas, Jose L., Ogayar, Carlos J., Feito, Francisco R., and Jurado, Juan M.

Abstract

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.

Published
2023
Full Text
View/download PDF

20. Guided Modeling of Natural Scenarios: Vegetation and Terrain

Author

Collado, José Antonio, López, Alfonso, Pérez, Juan Roberto Jiménez, Ortega, Lidia M., Feito, Francisco R., and Jurado, Juan M.

Subjects
+Point-based+models%22">CCS Concepts: Computing methodologies --> Point-based models, Modeling methodologies, based models, Computing methodologies, Point
Abstract

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., Modeling, José Antonio Collado, Alfonso López, Juan Roberto Jiménez Pérez, Lidia M. Ortega, Francisco R. Feito, and Juan M. Jurado

Published
2022
Full Text
View/download PDF

24. Generation Process of Intrinsic Images Dataset Through Physically-based Rendering

Author

Rodríguez, Ignacio Moral, López, Alfonso, Jiménez-Perez, J. Roberto, Feito, Francisco R., Ortega, Lidia, and Jurado, Juan M.

Subjects
Computer graphics, Computing methodologies, Intrinsic decomposition, Rendering
Abstract

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., Spanish Computer Graphics Conference (CEIG), Short Papers, 35, 38, Ignacio Moral Rodríguez, Alfonso López, J. Roberto Jiménez-Perez, Francisco R. Feito, Lidia Ortega, and Juan M. Jurado, CCS Concepts: Computing methodologies --> Computer graphics; Intrinsic decomposition; Rendering

Published
2021
Full Text
View/download PDF

28. Semantic Segmentation of Natural Materials on a Point Cloud Using Spatial and Multispectral Features.

Author

Jurado JM, Cárdenas JL, Ogayar CJ, Ortega L, and Feito FR

Subjects
Algorithms, Ecosystem, Plant Leaves, Semantics, Imaging, Three-Dimensional methods, Photography methods
Abstract

The characterization of natural spaces by the precise observation of their material properties is highly demanded in remote sensing and computer vision. The production of novel sensors enables the collection of heterogeneous data to get a comprehensive knowledge of the living and non-living entities in the ecosystem. The high resolution of consumer-grade RGB cameras is frequently used for the geometric reconstruction of many types of environments. Nevertheless, the understanding of natural spaces is still challenging. The automatic segmentation of homogeneous materials in nature is a complex task because there are many overlapping structures and an indirect illumination, so the object recognition is difficult. In this paper, we propose a method based on fusing spatial and multispectral characteristics for the unsupervised classification of natural materials in a point cloud. A high-resolution camera and a multispectral sensor are mounted on a custom camera rig in order to simultaneously capture RGB and multispectral images. Our method is tested in a controlled scenario, where different natural objects coexist. Initially, the input RGB images are processed to generate a point cloud by applying the structure-from-motion (SfM) algorithm. Then, the multispectral images are mapped on the three-dimensional model to characterize the geometry with the reflectance captured from four narrow bands (green, red, red-edge and near-infrared). The reflectance, the visible colour and the spatial component are combined to extract key differences among all existing materials. For this purpose, a hierarchical cluster analysis is applied to pool the point cloud and identify the feature pattern for every material. As a result, the tree trunk, the leaves, different species of low plants, the ground and rocks can be clearly recognized in the scene. These results demonstrate the feasibility to perform a semantic segmentation by considering multispectral and spatial features with an unknown number of clusters to be detected on the point cloud. Moreover, our solution is compared to other method based on supervised learning in order to test the improvement of the proposed approach.

Published
2020
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results