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Semi-regular Remeshing based trust region spherical geometry image for 3D deformed mesh used MLWNN

机译:基于MLWNN的3D变形网格的基于半正则网格划分的信任区域球形几何图像

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Triangular surface are now widely used for modeling three-dimensional object, since these models are very high resolution and the geometry of the mesh is often very dense, it is then necessary to remesh this object to reduce their complexity, the mesh quality (connectivity regularity) must be ameliorated. In this paper, we review the main methods of semi-regular remeshing of the state of the art, given the semi-regular remeshing is mainly relevant for wavelet-based compression, then we present our method for re-meshing based trust region spherical geometry image to have good scheme of 3d mesh compression used to deform 3D meh based on Multi library Wavelet Neural Network structure (MLWNN). Experimental results show that the progressive re-meshing algorithm capable of obtaining more compact representations and semi-regular objects and yield an efficient compression capabilities with minimal set of features used to have good 3D deformation scheme.
机译:三角形表面现在被广泛用于三维物体的建模,因为这些模型具有很高的分辨率,并且网格的几何形状通常非常密集,因此有必要重新网格化此对象以降低其复杂性,网格质量(连通性规则性) )必须加以改善。在本文中,我们回顾了现有技术的半规则重新网格化的主要方法,因为半规则重新网格化主要与基于小波的压缩有关,然后我们提出了基于网格划分的信任区域球面几何的重新网格化方法图像具有基于多库小波神经网络结构(MLWNN)的3D网格变形的良好3d网格压缩方案。实验结果表明,渐进式重新网格划分算法能够获得更紧凑的表示形式和半规则对象,并具有有效的压缩功能,并且具有用于具有良好3D变形方案的最少特征集。

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