Journal
COMPUTER VISION AND IMAGE UNDERSTANDING
Volume 157, Issue -, Pages 43-54Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.cviu.2016.09.011
Keywords
3D reconstruction; Range data; Massive point clouds; Error-aware; reconstruction; Compression; Panoramas; Interactive inspection
Funding
- Spanish Ministry of Economy and Competitiveness
- FEDER [TIN201452211-C2-1-R]
- Spanish Ministry of Education, Culture and Sports [FPU14/00725]
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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. (C) 2016 Elsevier Inc. All rights reserved.
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