4 results
Search Results
Now showing 1 - 4 of 4
Item 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, AdriánThe 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.Item Multispectral Registration, Undistortion and Tree Detection for Precision Agriculture(The Eurographics Association, 2019) Ruiz, Alfonso López; Rodríguez, Juan Manuel Jurado; Anguita, Carlos Javier Ogayar; Higueruela, Francisco Ramón Feito; Casas, Dan and Jarabo, AdriánMulti-lens multispectral cameras allow us to record multispectral information for a whole area of terrain, even though we may only need the vegetation data. Based on the intensity of each multispectral image we can retrieve the contours of the trees that appear on the recorded terrain. However, multispectral cameras use a physically different lens for each range of wavelengths and misregistration effects could appear due to the different viewing positions. As these types of lenses are dedicated to capture larger areas of terrain, their focal distance is lower and because of this we get what is called a fisheye distortion. Therefore if we want to retrieve the shape of each tree and its multispectral data we need to process the channels so them all are representated as undistorted images under a same reference system.Item Prototype of an Automatic Registration System for the Remains of Archaeological Sites(The Eurographics Association, 2019) Ruiz, José Luis López; Alvarado, Lidia Ortega; Higueruela, Francisco Ramón Feito; Casas, Dan and Jarabo, AdriánData acquisition in archaeological excavations is a time consuming task developed on site that must be completed later with the information stored in the database. In this paper, we introduce the work in progress for the automatic data acquisition and record of artifacts on the archaeological site. The shape of the object is recognized automatically and converted to vectorial format. Then, if this contour is recognized as object of interest, the initial shape and specific position is registered and inserted in a spatial database. In this process, some techniques associated to Augmented Reality and boundary detection are used.Item 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, AntonioThis 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.