162110 20251017144612.0 doi 10.1109/TVCG.2025.3562696 sideral 144709 ART-2025-144709 eng Collado, José A. Virtualized Point Cloud Rendering 2025 Access copy available to the general public Unrestricted Remote sensing technologies, such as LiDAR, produce billions of points that commonly exceed the storage capacity of the GPU, restricting their processing and rendering. Level of detail (LoD) techniques have been widely investigated, but building the LoD structures is also time-consuming. This study proposes a GPU-driven culling system focused on determining the number of points visible in every frame. It can manipulate point clouds of any arbitrary size while maintaining a low memory footprint in both the CPU and GPU. Instead of organizing point clouds into hierarchical data structures, these are split into groups of points sorted using the Hilbert encoding. This alternative alleviates the occurrence of anomalous groups found in Morton curves. Instead of keeping the entire point cloud in the GPU, points are transferred on demand to ensure real-time capability. Accordingly, our solution can manipulate huge point clouds even in commodity hardware with low memory capacities. Moreover, hole filling is implemented to cover the gaps derived from insufficient density and our LoD system. Our proposal was evaluated with point clouds of up to 18 billion points, achieving an average of 80 frames per second (FPS) without perceptible quality loss. Relaxing memory constraints further enhances visual quality while maintaining an interactive frame rate. We assessed our method on real-world data, comparing it against three state-ofthe- art methods, demonstrating its ability to handle significantly larger point clouds. The code is available on https://github.com/Krixtalx/Nimbus. info:eu-repo/grantAgreement/ES/MICIU/JDC2023-051785-I info:eu-repo/grantAgreement/ES/MICIU/PID2021-126339OB-I00 info:eu-repo/grantAgreement/ES/MICIU/PID2022-137938OA-I00 info:eu-repo/grantAgreement/ES/MICIU/TED2021-132120B-I00 info:eu-repo/semantics/openAccess by https://creativecommons.org/licenses/by/4.0/deed.es info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion López, Alfonso Universidad de Zaragoza (orcid)0000-0003-1423-9496 Jurado, Juan M. Jiménez, J. Roberto 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. (2025), [14 pp.] IEEE trans. vis. comput. graph. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 1077-2626 1613225 http://zaguan.unizar.es/record/162110/files/texto_completo.pdf Versión publicada 3640104 http://zaguan.unizar.es/record/162110/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:162110 articulos driver 2025-10-17-14:17:54 ARTICLE