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.