Viewpoint selection is an important research field in computer graphics, which analyses a model and generates viewpoints. These viewpoints fit well with human preferences, and contain as much as possible salient model features. But until now, the evaluation of viewpoint selection results has not yet been quantized. This paper designs and implements a quantized viewpoint selection benchmark. At first, it collects human viewpoint selection results of 30 persons observing 45 models, and defines the standard-human-viewpoint of each model according to these results. Then, it analyzes the consistence and stability of human viewpoint selection. At last, it implements five (three kinds) representative viewpoint selection algorithms, analyze and compare their results with standardhuman- viewpoints. It also analyzes the performance differences between each kind of algorithm dealing various types of models. The experimental results show that human viewpoint selection has good consistence and stability, and differs on various types of models. All tested algorithms don’t have great performance difference, but mutual information based method and viewing plane feature measurement method are a little better than others. Each kind of algorithm performs best at different type of models.