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Computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models

Photorealistic 3D models are used for visualization, interpretation and spatial measurement in many disciplines, such as cultural heritage, archaeology and geoscience. Using modern image- and laser-based 3D modelling techniques, it is normal to acquire more data than is finally used for 3D model texturing, as images may be acquired from multiple positions, with large overlap, or with different cameras and lenses. Such redundant image sets require sorting to restrict the number of images, increasing the processing efficiency and realism of models. However, selection of image subsets optimized for texturing purposes is an example of complex spatial analysis. Manual selection may be challenging and time-consuming, especially for models of rugose topography, where the user must account for occlusions and ensure coverage of all relevant model triangles. To address this, this paper presents a framework for computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models. The framework was created to offer algorithms for candidate image subset selection, whilst supporting refinement of subsets in an intuitive and visual manner. Automatic image sorting was implemented using algorithms originating in computer science and information theory, and variants of these were compared using multiple 3D models and covering image sets, collected for geological applications. The image subsets provided by the automatic procedures were compared to manually selected sets and their suitability for 3D model texturing was assessed. Results indicate that the automatic sorting algorithms are a promising alternative to manual methods. An algorithm based on a greedy solution to the weighted set-cover problem provided image sets closest to the quality and size of the manually selected sets. The improved automation and more reliable quality indicators make the photorealistic model creation workflow more accessible for application experts, increasing the user’s confidence in the final textured model completeness

This work is supported by the Research Council of Norway’s Petromaks programme, with support from FORCE (grant 193059) and the Statoil Academia Agreement (grant 200512). A. Rittersbacher is thanked for documenting the manually selected image sets. Riegl GmbH is thanked for providing continued software support. This work has been partially supported by grants from the Spanish Government (Nr TIN2010-21089-C03-01) and from the Catalan Government (Nr 2009-SGR-643)

© Computers and Geosciences, 2013, vol. 52, p. 281-291

Elsevier

Manager: Ministerio de Ciencia e Innovación (Espanya)
Generalitat de Catalunya. Agència de Gestió d’Ajuts Universitaris i de Recerca
Author: Sima, Aleksandra Anna
Bonaventura Brugués, Xavier
Feixas Feixas, Miquel
Sbert, Mateu
Howell, John Anthony
Viola, Ivan
Buckley, Simon John
Date: 2013
Abstract: Photorealistic 3D models are used for visualization, interpretation and spatial measurement in many disciplines, such as cultural heritage, archaeology and geoscience. Using modern image- and laser-based 3D modelling techniques, it is normal to acquire more data than is finally used for 3D model texturing, as images may be acquired from multiple positions, with large overlap, or with different cameras and lenses. Such redundant image sets require sorting to restrict the number of images, increasing the processing efficiency and realism of models. However, selection of image subsets optimized for texturing purposes is an example of complex spatial analysis. Manual selection may be challenging and time-consuming, especially for models of rugose topography, where the user must account for occlusions and ensure coverage of all relevant model triangles. To address this, this paper presents a framework for computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models. The framework was created to offer algorithms for candidate image subset selection, whilst supporting refinement of subsets in an intuitive and visual manner. Automatic image sorting was implemented using algorithms originating in computer science and information theory, and variants of these were compared using multiple 3D models and covering image sets, collected for geological applications. The image subsets provided by the automatic procedures were compared to manually selected sets and their suitability for 3D model texturing was assessed. Results indicate that the automatic sorting algorithms are a promising alternative to manual methods. An algorithm based on a greedy solution to the weighted set-cover problem provided image sets closest to the quality and size of the manually selected sets. The improved automation and more reliable quality indicators make the photorealistic model creation workflow more accessible for application experts, increasing the user’s confidence in the final textured model completeness
This work is supported by the Research Council of Norway’s Petromaks programme, with support from FORCE (grant 193059) and the Statoil Academia Agreement (grant 200512). A. Rittersbacher is thanked for documenting the manually selected image sets. Riegl GmbH is thanked for providing continued software support. This work has been partially supported by grants from the Spanish Government (Nr TIN2010-21089-C03-01) and from the Catalan Government (Nr 2009-SGR-643)
Format: application/pdf
Citation: 017228
ISSN: 0098-3004
Document access: http://hdl.handle.net/10256/11674
Language: eng
Publisher: Elsevier
Collection: MICINN/PN 2011-2013/TIN2010-21089-C03-01
AGAUR/2009-2014/2009 SGR-643
Reproducció digital del document publicat a: http://dx.doi.org/10.1016/j.cageo.2012.11.004
Articles publicats (D-IMA)
Is part of: © Computers and Geosciences, 2013, vol. 52, p. 281-291
Rights: Tots els drets reservats
Subject: Visualització tridimensional (Informàtica)
Three-dimensional display systems
Informació, Teoria de la
Information theory
Imatgeria tridimensional
Three-dimensional imaging
Title: Computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models
Type: info:eu-repo/semantics/article
Repository: DUGiDocs

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