Title:
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Error-aware construction and rendering of multi-scan panoramas from massive point clouds
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Author:
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Comino Trinidad, Marc; Andújar Gran, Carlos Antonio; Chica Calaf, Antonio; Brunet Crosa, Pere
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. ViRVIG - Grup de Recerca en Visualització, Realitat Virtual i Interacció Gràfica |
Abstract:
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Obtaining 3D realistic models of urban scenes from accurate range data is nowadays an important research topic, with applications in a variety of fields ranging from Cultural Heritage and digital 3D archiving to monitoring of public works. Processing massive point clouds acquired from laser scanners involves a number of challenges, from data management to noise removal, model compression and interactive visualization and inspection. In this paper, we present a new methodology for the reconstruction of 3D scenes from massive point clouds coming from range lidar sensors. Our proposal includes a panorama-based compact reconstruction where colors and normals are estimated robustly through an error-aware algorithm that takes into account the variance of expected errors in depth measurements. Our representation supports efficient, GPU-based visualization with advanced lighting effects. We discuss the proposed algorithms in a practical application on urban and historical preservation, described by a massive point cloud of 3.5 billion points. We show that we can achieve compression rates higher than 97% with good visual quality during interactive inspections. |
Abstract:
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Peer Reviewed |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Aplicacions de la informàtica::Aplicacions informàtiques a la física i l‘enginyeria -Three-dimensional imaging -Optical data processing -Point set theory -3D reconstruction -range data -massive point clouds -error-aware reconstruction -compression -panoramas -interactive inspection -Imatges tridimensionals -Processament òptic de dades -Conjunts, Teoria de |
Rights:
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Document type:
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Article - Submitted version Article |
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