The question of what are good views of a 3D object has been addressed by numerous researchers in perception, computer vision, and computer graphics. This has led to a large variety of measures for the goodness of views as well as some special-case viewpoint selection algorithms. In this article, we leverage the results of a large user study to optimize the parameters of a general model for viewpoint goodness, such that the fitted model can predict people's preferred views for a broad range of objects. Our model is represented as a combination of attributes known to be important for view selection, such as projected model area and silhouette length. Moreover, this framework can easily incorporate new attributes in the future, based on the data from our existing study. We demonstrate our combined goodness measure in a number of applications, such as automatically selecting a good set of representative views, optimizing camera orbits to pass through good views and avoid bad views, and trackball controls that gently guide the viewer towards better views.

@article{Secord:2011:PMO,
   author = "Adrian Secord and Jingwan Lu and Adam Finkelstein and Manish Singh and
      Andrew Nealen",
   title = "Perceptual models of viewpoint preference",
   journal = "ACM Transactions on Graphics",
   year = "2011",
   month = oct,
   volume = "30",
   number = "5"
}