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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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    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.