3DOR 15

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https://diglib.eg.org/handle/10.2312/12281

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Browsing 3DOR 15 by Subject "H.3.3 [Computer Graphics]"

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    3D Object Retrieval with Parametric Templates
    (The Eurographics Association, 2015) Getto, Roman; Fellner, Dieter W.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    We propose a 3D object retrieval system which uses parametric templates as prior knowledge for the retrieval. A parametric template represents an object-domain and a semantic concept like 'chair' or 'plane' or a more specific concept like 'dining-char' or 'biplane'. The template can be specified at a general or specific level and can even equal actual retrieved objects. The parametric template is composed of several input parameters and an operation chain which constructs an object. Different parameter combinations lead to different object instances. We combine and evaluate a paramteric template with different descriptors. Our results show that the usage of parametric templates can raise the retrieval performance significantly.
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    Non-rigid 3D Shape Retrieval
    (The Eurographics Association, 2015) Lian, Z.; Zhang, J.; Choi, S.; ElNaghy, H.; El-Sana, J.; Furuya, T.; Giachetti, A.; Guler, R. A.; Lai, L.; Li, C.; Li, H.; Limberger, F. A.; Martin, R.; Nakanishi, R. U.; Neto, A. P.; Nonato, L. G.; Ohbuchi, R.; Pevzner, K.; Pickup, D.; Rosin, P.; Sharf, A.; Sun, L.; Sun, X.; Tari, S.; Unal, G.; Wilson, R. C.; I. Pratikakis and M. Spagnuolo and T. Theoharis and L. Van Gool and R. Veltkamp
    Non-rigid 3D shape retrieval has become a research hotpot in communities of computer graphics, computer vision, pattern recognition, etc. In this paper, we present the results of the SHREC'15 Track: Non-rigid 3D Shape Retrieval. The aim of this track is to provide a fair and effective platform to evaluate and compare the performance of current non-rigid 3D shape retrieval methods developed by different research groups around the world. The database utilized in this track consists of 1200 3D watertight triangle meshes which are equally classified into 50 categories. All models in the same category are generated from an original 3D mesh by implementing various pose transformations. The retrieval performance of a method is evaluated using 6 commonly-used measures (i.e., PR-plot, NN, FT, ST, E-measure and DCG.). Totally, there are 37 submissions and 11 groups taking part in this track. Evaluation results and comparison analyses described in this paper not only show the bright future in researches of non-rigid 3D shape retrieval but also point out several promising research directions in this topic.