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

Aleksandra Sima, Xavier Bonaventura, Miquel Feixas , Mateu Sbert, John Howell, Ivan Viola, Simon Buckley

ARTICLE, Computers and Geosciences, 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.

Published

Computers and Geosciences

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BibTeX

@article{Sima13Computer,
   author = {Aleksandra Sima and Xavier Bonaventura and Miquel Feixas
             and Mateu Sbert and John Howell and Ivan Viola and Simon Buckley},
    title = {Computer-aided image geometry analysis and subset selection for 
             optimizing texture quality in photorealistic models},
  journal = {Computers and Geosciences},
     year = {2013},
   volume = {52},
    pages = {281-291},
      doi = {10.1016/j.cageo.2012.11.004},
      url = {http://www.sciencedirect.com/science/article/pii/S0098300412003743},
 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 

             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.},


}






 Last Modified: Jean-Paul Balabanian, 2014-09-26