已有视图度量无法同时描述3维模型整体和局部细节特征,因此难以得到理想的最优视图。提出一种结合统计分类和视图边缘细节特征的最优视图提取算法。首先,采用Adaboost进行样例学习,通过最优视图之间的几何特征相似性得到候选视图集合。然后,定义边缘分布熵对候选视图进行局部特征分析,用以提取最优视图,从而使提取出来的最优视图能够有效描述出3维模型的结构特征和内在细节特征,符合人类视觉感知效果。最后,通过3维模型数据库对算法进行统计分析。实验结果表明,本文算法要优于类似的最优视图算法。

Existing measurements cannot capture global and local features to get the best view of a 3D model. In this paper, we address the problem, and propose a multi-stage method by combining example-learning and edge feature of views. The whole algorithm mainly consists of the following steps. First, Adaboost is applied to select candidate views of the input 3D model by statistical classification and shape similarity. Second, edge information of these views is extracted to define the entropy. It can effectively measure how the candidate views capture local features. Finally, the best viewpoint is selected using a weighted combination of shape similarity and entropy. In our experiments, the algorithm is verified on a 3D model benchmark.