PG2013short

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https://diglib.eg.org/handle/10.2312/998

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Browsing PG2013short by Subject "and object representations"

Now showing 1 - 2 of 2
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    Bezier Crust on Quad Subdivision Surface
    (The Eurographics Association, 2013) Wang, Jianzhong; Cheng, Fuhua; Bruno Levy and Xin Tong and KangKang Yin
    Subdivision surfaces have been widely used in computer graphics and can be classified into two categories, approximating and interpolatory. Representative approximating schemes are Catmull-Clark (quad) and Loop (triangular). Although widely used, one issue remains with the approximating schemes, i.e., the process of interpolating a set of data points is a global process so it is difficult to interpolate large data sets. In this paper, we present a local interpolation scheme for quad subdivision surfaces through appending a G2 Bezier crust to the underlying surface, and show that this local interpolation scheme does not change the curvatures across the boundaries of underlying subdivision patches, therefore, one obtains high quality interpolating limit surfaces for engineering and graphics applications efficiently.
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    Non-rigid 3D Shape Retrieval via Sparse Representation
    (The Eurographics Association, 2013) Wan, Lili; Li, Shuai; Miao, Zhenjiang J.; Cen, Yigang G.; Bruno Levy and Xin Tong and KangKang Yin
    Shape descriptor design is an important but challenging problem for non-rigid 3D shape retrieval. Recently, bagof- words based methods are widely used to integrate a model's local shape descriptors into a global histogram. In this paper, we present a new method to pool the local shape descriptors into a global shape descriptor by means of sparse representation. Firstly, we employ heat kernel signature (HKS) to depict the multi-scale local shape. Then, for each model in the training dataset, we take the HKSs corresponding to its mesh vertices to serve as training signals, and thus an over-complete dictionary can be learned from them. Finally, the HKSs of each 3D model are sparsely coded based on the learned dictionary, and such sparse representations can be further integrated to form an object-level shape descriptor. Moreover, we conduct extensive experiments on the state-of-the-art benchmarks, wherein comprehensive evaluations state our method can achieve better performance than other bag-of-words based approaches.