【摘要】 网格纹理平滑技术要求既能保持模型大尺度结构特征又能去除模型小尺度纹理.然而当模型小尺度纹理与噪声相差较大时,大多数网格光顺算法会将网格纹理识别为特征加以保持,而无法有效将其去除;现有的基于谱分析的网格光顺方法尽管能有效去除网格纹理,但又无法同时保持模型大尺度结构特征.为解决该问题,本文提出一种基于混合频谱信号编码的低通过滤网格纹理平滑算法.首先采用基于视觉感知的特征识别方法,准确区分模型大尺度与小尺度特征.然后,基于顶点特征尺度,采用差异性频谱信号编码的方式进行几何信息重建,最终实现在保持网格模型大尺度结构特征的同时有效去除小尺度纹理.算法解决了现有网格光顺方法在模型小尺度纹理特征与噪声有明显区别时,无法有效去除纹理的问题;并且也解决了现有基于谱分析的网格光顺方法无法在去除模型小尺度纹理的同时,又能保持模型大尺度特征的矛盾.实验结果验证了算法的有效性.更多还原

【Abstract】 More and more application systems,such as mesh model reuse,3 D texture mapping,3 D data transmission,mesh compression,simplification,3 D real-time rendering and so on,have put forward requirements for the 3 D mesh textures smoothing.The technology of the mesh texture smoothing is expected to both reduce the small-scale detail texture features and keep the large-scale intrinsic structures.Traditional mesh smoothing methods tend to focus on removing high frequency random noise and preserving the features.In case of the small-scale textures are quite different from noise,those methods tend to regard them as features to preserve them rather than eliminate them.The existing mesh smoothing methods based on spectral analysis can smooth out all of the small-scale textures,but also over-smooth the large-scale structural features on the models.To solve these problems,the paper proposed a low-pass filter based on the hybrid spectral encoding.Firstly,a feature recognition method based on the visual awareness is used to accurately recognize the scale-features on the models.The mesh Laplace-Beltrami operator is constructed and the base functions are obtained through the spectral analysis.Regarding the geometric informations of the vertices as signals,a spectral space is constructed by projecting the geometric informations to the base functions.Using the low-frequency coefficients,a smooth base surface of the original mesh model is constructed,which is regarded as the three-dimensional datum of the original mesh model.The height between the mesh vertex and the three-dimensional datum is calculated to obtain the visual importance of the vertex.The vertex with the height value larger than a threshold is defined as the large-scale feature vertex.Next,a hybrid spectral encoding method is proposed to reconstruct the mesh model.There are two frequencies setted appropriately:one is the higher frequency β which is used to remove high frequency noise and construct structural features,and the other is the lower frequency a which is used to remove detail textures.On the large-scale vertex,the high-frequency coefficient β is adopted to reconstruct the geometry information;and on the small-scale vertex,the low-frequency coefficient a is adopted correspondingly.The result is that the large-scale structure features are preserved effectively,and at the same time the small-scale textures are removed completely.The major contribution of the proposed method is that it presents a hybrid spectral encoding framework which can adopt different frequency coefficients to construct the vertex geometry according to different scale features,and the aim of removing the small-scale features and simultaneously maintaining the large-scale structural features has been achieved.The proposed method solves the problem that the existing mesh smoothing methods cannot effectively remove the small-scale textures when the small-scale textures differ significantly from the noise.And it also solves the contradiction that the existing spectral mesh smoothing method cannot maintain the large-scale features when removing the small-scale features,and cannot remove the small-scale features when maintaining the large-scale features as much as possible.The paper demonstrates the effectiveness of the proposed method compared with many state-of-the-art mesh smoothing methods,the experimental results verify the superiority of the proposed method.更多还原