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

Aleksandra Anna Sima, Xavier Bonaventura, Miquel Feixas, Mateu Sbert, John Anthony Howell, Ivan Viola, Simon John Buckley
Computer-aided image geometry analysis and subset selection for optimizing texture quality in photorealistic models
Computers & Geosciences, 52():281-291, March 2013.

Information

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.

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BibTeX

@article{Viola_Ivan_2013_CAI,
  title =      "Computer-aided image geometry analysis and subset selection
               for optimizing texture quality in photorealistic models",
  author =     "Aleksandra Anna Sima  and Xavier Bonaventura  and Miquel
               Feixas and Mateu Sbert and John Anthony Howell  and Ivan
               Viola and Simon John Buckley",
  year =       "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.",
  month =      mar,
  journal =    "Computers & Geosciences",
  volume =     "52",
  pages =      "281--291",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2013/Viola_Ivan_2013_CAI/",
}