VMV14

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https://diglib.eg.org/handle/10.2312/7753

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Browsing VMV14 by Subject "Computational Geometry and Object Modeling"

Now showing 1 - 2 of 2
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    Comparative Evaluation of Feature Line Techniques for Shape Depiction
    (The Eurographics Association, 2014) Lawonn, Kai; Baer, Alexandra; Saalfeld, Patrick; Preim, Bernhard; Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urban
    This paper presents a qualitative evaluation of feature line techniques on various surfaces. We introduce the most commonly used feature lines and compare them. The techniques were analyzed with respect to the degree of realism in comparison with a shaded image with respect to the aesthetic impression they create. First, a pilot study with 20 participants was conducted to make an inquiry about their behavior and the duration. Based on the result of the pilot study, the final evaluation was carried out with 129 participants. We evaluate and interpret the trial results by using the Schulze method and give recommendations for which kind of surface, which feature line technique is most appropriate.
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    SOAR: Stochastic Optimization for Affine global point set Registration
    (The Eurographics Association, 2014) Agus, Marco; Gobbetti, Enrico; Villanueva, Alberto Jaspe; Mura, Claudio; Pajarola, Renato; Jan Bender and Arjan Kuijper and Tatiana von Landesberger and Holger Theisel and Philipp Urban
    We introduce a stochastic algorithm for pairwise affine registration of partially overlapping 3D point clouds with unknown point correspondences. The algorithm recovers the globally optimal scale, rotation, and translation alignment parameters and is applicable in a variety of difficult settings, including very sparse, noisy, and outlierridden datasets that do not permit the computation of local descriptors. The technique is based on a stochastic approach for the global optimization of an alignment error function robust to noise and resistant to outliers. At each optimization step, it alternates between stochastically visiting a generalized BSP-tree representation of the current solution landscape to select a promising transformation, finding point-to-point correspondences using a GPU-accelerated technique, and incorporating new error values in the BSP tree. In contrast to previous work, instead of simply constructing the tree by guided random sampling, we exploit the problem structure through a low-cost local minimization process based on analytically solving absolute orientation problems using the current correspondences. We demonstrate the quality and performance of our method on a variety of large point sets with different scales, resolutions, and noise characteristics.